Programming and scripting languages: general Books

Book SynopsisY. Daniel Liang earned his Ph.D. in Computer Science from the University of Oklahoma, USA in 1991, and an MS and BS in Computer Science from Fudan University, China, in 1986 and 1983. Prior to joining Armstrong, he was Associate Professor of Computer Science at Purdue University in Fort Wayne, where he twice received the Excellence in Research Award. Dr. Liang was trained in theoretical computer science. He was active in graph algorithms from 1990 to 1995 and published more than ten papers in several established journals such as SIAM Journal on Computing, Discrete Applied Mathematics, Acta Informatics, and Information Processing Letters. Since 1996, he has devoted to writing texts and published more than thirty books with Prentice Hall. His popular computer science texts are widely adopted in the world. Dr. Liang was elected a Java Champion in 2005 by Sun Microsystems and has given lectures on Java internationally.Table of Contents Introduction to Computers, Programs, and Python Elementary Programming Selections Mathematical Functions, Strings, and Objects Loops Functions Lists Multidimensional Lists Objects and Classes Basic GUI Programming Using Tkinter Advanced GUI Programming Using Tkinter Inheritance and Polymorphism Files and Exception Handling Tuples, Sets, and Dictionaries Recursion Developing Efficient Algorithms Sorting Linked Lists, Stacks, Queues, and Priority Queues Binary Search Trees AVL Trees Hashing Graphs and Applications Weighted Graphs and Applications Appendix A. Python Keywords Appendix B. The ASCII Character Set Appendix C. Number Systems Appendix D. Command Line Arguments Appendix E. Regular Expressions Appendix F. Bitwise Operations Appendix G. The Big-O, Big-Omega, and Big-Theta Notations Appendix H. Operator Precedence Chart Symbol Index Supplemental Material Glossary

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Book SynopsisD3 allows developers to create scalable graphs for any modern browser. They start with a structure, dataset, or algorithm and programmatically generate static, interactive, or animated images that responsively scale to any screen. D3.js in Action, Second Edition is completely revised and updated for D3 v4 and ES6. It's a practical tutorial for creating interactive graphics and data-driven applications using D3. Readers will start with in-depth explanations of D3's out-of-the-box layouts, along with dozens of realworld use cases that align with different types of visualizations. By the end, readers will be ready to integrate D3.js into their web development process and add data visualization to transform any site or internal application. Key Features: · Completely revised and updated · Practical tutorial · In-depth explanations Readers need basic HTML, CSS, and JavaScript skills. No experience with D3 or SVG is required. About the Technology: D3.js is a JavaScript library that allows data to be represented graphically.

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Book SynopsisThis fast-moving tutorial introduces you to OCaml, an industrial-strength programming language designed for expressiveness, safety, and speed. Through the book''s many examples, you''ll quickly learn how OCaml stands out as a tool for writing fast, succinct, and readable systems code using functional programming. Real World OCaml takes you through the concepts of the language at a brisk pace, and then helps you explore the tools and techniques that make OCaml an effective and practical tool. You''ll also delve deep into the details of the compiler toolchain and OCaml''s simple and efficient runtime system. This second edition brings the book up to date with almost a decade of improvements in the OCaml language and ecosystem, with new chapters covering testing, GADTs, and platform tooling. This title is also available as open access on Cambridge Core, thanks to the support of Tarides. Their generous contribution will bring more people to OCaml.Trade Review'An invaluable guide to effective OCaml programming! With extended and updated coverage of key libraries and tools, it covers the language concepts and will teach you not only how to program in OCaml, but also how to develop efficient systems applications in this language.' Xavier Leroy, Collège de France and INRIA'OCaml is widely known as an elegant language based on cutting-edge ideas. But this book focuses on use of OCaml as a powerful tool for the software industry. Using a series of hands-on examples, it shows the reader how to use advanced features from the OCaml ecosystem (types, modules, testing frameworks, libraries, package management, build tools, etc.) to solve practical problems. Real World OCaml is my go-to reference for learning how to develop real-world software systems in OCaml.' Nate Foster, Cornell UniversityTable of Contents1. Prologue; Part I. Language Concepts: 2. A guided tour; 3. Variables and functions; 4. Lists and patterns; 5. Files, modules, and programs; 6. Records; 7. Variants; 8. Error handling; 9. Imperative programming; 10. GADTs; 11. Functors; 12. First-class Modules; 13. Objects; 14. Classes; Part II. Tools and Techniques: 15. Maps and hash tables; 16. Command-line parsing; 17. Concurrent programming with Async; 18. Testing; 19. Handling JSON data; 20. Parsing with Ocamllex and Menhir; 21. Data serialization with S-expressions; 22. The OCaml platform; Part III. The Compiler and Runtime System: 23. Foreign function interface; 24. Memory representation of values; 25. Understanding the garbage collector; 26. The compiler frontend: parsing and type checking; 27. The compiler backend: bytecode and native code; References; Index.

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Book SynopsisThis philosophy-of-programming guide presents a unique and entertaining take on how to think about programming. A collection of 21 pragmatic rules, each presented in a standalone chapter, captures the essential wisdom that every freshly minted programmer needs to know and provides thought-provoking insights for more seasoned programmers.

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Book SynopsisOrganizations that conduct business online are constantly engaged in a cat-and-mouse game with these invaders. In this practical book, Gilit Saporta and Shoshana Maraney draw on their experience of fraud fighting to provide best practices, methodologies, and tools to help your organization detect and prevent fraud and other malicious activities.

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Book SynopsisThe design of application programming interfaces can affect the behavior, capabilities, stability, and ease of use of end-user applications. This book helps you learn how to design a good API for large-scale long-term projects. With C++ code to illustrate each concept, it covers the various strategies of API development.Trade Review"Martin Reddy draws from his experience on large scale, collaborative software projects to present patterns and practices that provide real value to individual developers as well as organizations. API Design for C++ explores often overlooked issues, both technical and non- technical, contributing to successful design decisions that produce high quality, robust, and long-lived APIs." --Eric Gregory, Software Architect, Pixar Animation Studios"Intended for programmers with intermediate to advanced skills in the C++ programming language, this guide to the building of useful and robust application programming interfaces (APIs) provides practical instruction for software engineers developing systems on which downstream software engineers depend. The work provides a methodical approach to API design covering solution based API design, performance, versioning, documentation, testing, scripting, extensibility and libraries. The work includes numerous illustrations and code examples and access to additional online resources is provided. Reddy is a software development consultant." --Book News, Reference & ResearchTable of ContentsIntroduction 1. Qualities 2. Patterns 3. Design 4. Styles 5. C++ Usage 6. Performance 7. Versioning 8. Documentation 9. Testing 10. Scripting 11. Extensibility Appendix A: Libraries Bibliography Index

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Book SynopsisAbout our author Eric Roberts is the Charles Simonyi Professor of Computer Science, emeritus, at Stanford University. Throughout his career, Roberts was a widely acknowledged leader in computer science education and received numerous national and international awards, including the Outstanding Contribution to Computer Science Education Award from the Association for Computing Machinery's Special Interest Group in Computer Science Education (ACM SIGCSE), the Karl V. Karlstrom Outstanding Educator Award from the ACM, and the Taylor Booth Education Award from the Institute for Electrical and Electronic Engineers Computer Society (IEEE-CS).Table of ContentsTable of Contents A Gentle Introduction 1.1 Introducing Karel 1.2 Teaching Karel to solve problems 1.3 Control statements 1.4 Stepwise refinement 1.5 Algorithms in Karel’s world Summary Review questions Exercises Introducing JavaScript 2.1 Data and types 2.2 Numeric data 2.3 Variables 2.4 Functions 2.5 String data 2.6 Running JavaScript in the browser 2.7 Testing and debugging 2.8 Software maintenance Summary Review questions Exercises Control Statements 3.1 Boolean Data 3.2 The if statement 3.3 The switch statement 3.4 The while statement 3.5 The for statement 3.6 Algorithmic programming 3.7 Avoiding fuzzy standards of truth Summary Review questions Exercises Simple Graphics 4.1 A graphical version of “Hello World” 4.2 Classes, objects, and methods 4.3 Graphical objects 4.4 The graphics window 4.5 Creating graphical applications Summary Review questions Exercises Functions 5.1 A quick review of functions 5.2 Libraries 5.3 A library to support randomness 5.4 The mechanics of function calls 5.5 Recursive functions Summary Review questions Exercises Writing Interactive Programs 6.1 First-class functions 6.2 A simple interactive example 6.3 Controlling properties of objects 6.4 Responding to mouse events 6.5 Timer-based animation 6.6 Expanding the graphics library Summary Review questions Exercises Strings 7.1 Binary representation 7.2 String operations 7.3 Classifying characters 7.4 Common string patterns 7.5 String applications 7.6 Reading from the console Summary Review questions Exercises Arrays 8.1 Introduction to arrays 8.2 Array operations 8.3 Using arrays for tabulation 8.4 Reading text from files 8.5 Multidimensional arrays 8.6 Image processing Summary Review questions Exercises Objects 9.1 Objects in JavaScript 9.2 Using objects as maps 9.3 Representing points 9.4 Rational numbers 9.5 Linking objects together Summary Review questions Exercises Designing Data Structures 10.1 Abstract data types 10.2 Implementing a token scanner 10.3 Efficiency and representation 10.4 Representing real-world data Summary Review questions Exercises Inheritance 11.1 Class hierarchies 11.2 Defining an employee hierarchy 11.3 Extending graphical classes 11.4 Decomposition and inheritance 11.5 Alternatives to inheritance Summary Review questions Exercises JavaScript and the Web 12.1 A simple interactive example 12.2 An expanded look at HTML 12.3 Controlling style using CSS 12.4 Connecting JavaScript and HTML 12.5 Storing data in the index.html file Summary Review questions Exercises

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Book SynopsisFortran marches on, remaining one of the principal programming languages used in high-performance scientific, numerical, and engineering computing. A series of significant revisions to the standard versions of the language have progressively enhanced its capabilities, and the latest standard - Fortran 2018 - includes many additions and improvements.This edition of Modern Fortran Explained expands on the last. Given the release of updated versions of Fortran compilers, the separate descriptions of Fortran 2003 and Fortran 2008 have been incorporated into the main text, which thereby becomes a unified description of the full Fortran 2008 version of the language. This clearer standard has allowed many deficiencies and irregularities in the earlier language versions to be resolved.Four new chapters describe the additional features of Fortran 2018, with its enhancements to coarrays for parallel programming, interoperability with C, IEEE arithmetic, and various other improvements.Written by Trade ReviewAn excellent, authoritative and complete reference book on the modern Fortran language, where all its possibilities are collected and any detail of the language can be clearly consulted. * European Mathematical Society *Well organized and equipped with good examples, the text can be used as both a tutorial and a reference. It describes each of the language elements and explains where FORTRAN 2018 differs from its predecessors. A thorough index is provided; all illustrations are clear and concise. The language used in descriptions and explanations throughout the book is clear and easy to follow. This book is an excellent resource for all practitioners. * F. H. Wild III, University of Rhode Island, CHOICE Connect *Table of Contents1: Whence Fortran? 2: Language elements 3: Expressions and assignments 4: Control constructs 5: Program units and procedures 6: Allocation of data 7: Array features 8: Specification statements 9: Intrinsic procedures and modules 10: Data transfer 11: Edit descriptors 12: Operations on external files 13: Advanced type parameter features 14: Procedure pointers 15: Object-oriented programming 16: Submodules 17: Coarrays 18: Floating-point exception handling 19: Interoperability with C 20: Fortran 2018 coarray enhancements 21: Fortran 2018 enhancements to interoperability with C 22: Fortran 2018 conformance with ISO/IEC/IEEE 60559:2011 23: Minor Fortran 2018 features

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Book SynopsisDouglas Bell and Mike Parr have many years experience teaching programming in the UK. They have written a number of programming books, including the bestselling Java for Students, Visual Basic for Students and C# for Students. They continue to teach and learn about programming with enthusiasm.Trade Review'The best book for my first year programming students'Gary Hill, The University of Northampton 'It is really hard to fault it or find a better book' Ken Chisholm, Edinburgh Napier University 'An excellent rewarding introduction to Java programming’Dr Simon Jones, University of StirlingTable of ContentsDetailed contentsIntroductionGuided tour The background to Java First programs Using graphics methods Variables and calculations Methods and parameters Using objects Selection Repetition Writing classes Inheritance Calculations Array lists Arrays Arrays - two dimensional String manipulation Exceptions Files and console applications Object-oriented design Program style Testing Debugging Threads Interfaces Programming in the large - packages Polymorphism Java in context Appendices: A. Java libraries B. The Abstract Window Toolkit C. Applets D. Glossary E. Rules for names F. Keywords G. Scope rules (visibility) H. Bibliography I. Installing and using Java Index

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Book SynopsisTable of ContentsPart I Fundamentals of ProgrammingChapter 1 Introduction to Computers, Programming, and C++ Chapter 2 Elementary Programming Chapter 3 Selections Chapter 4 Mathematical Functions, Characters, and Strings Chapter 5 Loops Chapter 6 Functions Chapter 7 Single-Dimensional Arrays and C-Strings Chapter 8 Multidimensional Arrays Part II Object-Oriented ProgrammingChapter 9 Objects and Classes Chapter 10 Object-Oriented Thinking Chapter 11 Pointers and Dynamic Memory Management Chapter 12 Templates, Vectors, and Stacks Chapter 13 File Input and Output Chapter 14 Operator Overloading Chapter 15 Inheritance and Polymorphism Chapter 16 Exception Handling Part III Data Structures and Advanced Topics Chapter 17 Recursion Bonus ChaptersChapter 18 Developing Efficient Algorithms Chapter 19 Sorting Chapter 20 Linked Lists, Queues, and Priority Queues Chapter 21 Binary Search Trees Chapter 22 STL Containers Chapter 23 STL Algorithms Chapter 24 Graph Applications Chapter 25 Weighted Graph Applications Chapter 26 AVL Trees and Splay Trees Appendixes Chapters 18-26 are bonus chapters available from http://www.pearsonhighered.com/liang. Login or register under VideoNotes and Web Chapters.

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Book SynopsisA Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source.Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools – data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn progrTable of ContentsAssumptions about the reader’s backgroundBook overview Introduction to R/Python Programming Calculator Variable and TypeFunctions Control flowsSome built-in data structures Revisit of variables Object-oriented programming (OOP) in R/Python Miscellaneous More on R/Python Programming Work with R/Python scripts Debugging in R/Python Benchmarking Vectorization Embarrassingly parallelism in R/Python Evaluation strategySpeed up with C/C++ in R/PythonA first impression of functional programming Miscellaneous data.table and pandasSQL Get started with data.table and pandas Indexing & selecting data Add/Remove/UpdateGroup by Join Random Variables, Distributions & Linear Regression A refresher on distributions Inversion sampling & rejection sampling Joint distribution & copula Fit a distribution Confidence intervalHypothesis testing Basics of linear regression Ridge regression Optimization in PracticeConvexity Gradient descent Root-finding General purpose minimization tools in R/Python Linear programming Miscellaneous Machine Learning - A gentle introduction Supervised learning Gradient boosting machine Unsupervised learning Reinforcement learning Deep Q-Networks Computational differentiation Miscellaneous

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Book SynopsisBecause secure code is more essential than ever, this new cookbook covers the full range of computer security needs with more than 200 ready-made solutions. It's a valuable asset for anyone who develops software with these highly popular programming languages.Trade Review"This is a book that's long overdue and makes for an interesting and deeply technical read on a topic that we should all core about more. Yes, it's limited to C and C++ readers, but with the majority of key applications being written in these languages that's where the biggest benefit can be had - give the sample chapter a read, and you'll soon be on your way to the books store to buy the rest of it." "A powerful and initially somewhat scary book that will quickly get you thinking about security while you program - as opposed to as an afterthought." - Paul Hudson, LinuxFormat, Christmas 03 - Rating 10/10 - Top Stuff AwardTable of ContentsForeword Preface 1. Safe Initialization 1.1 Sanitizing the Environment 1.2 Restricting Privileges on Windows 1.3 Dropping Privileges in setuid Programs 1.4 Limiting Risk with Privilege Separation 1.5 Managing File Descriptors Safely 1.6 Creating a Child Process Securely 1.7 Executing External Programs Securely 1.8 Executing External Programs Securely 1.9 Disabling Memory Dumps in the Event of a Crash 2. Access Control 2.1 Understanding the Unix Access Control Model 2.2 Understanding the Windows Access Control Model 2.3 Determining Whether a User Has Access to a File on Unix 2.4 Determining Whether a Directory Is Secure 2.5 Erasing Files Securely 2.6 Accessing File Information Securely 2.7 Restricting Access Permissions for New Files on Unix 2.8 Locking Files 2.9 Synchronizing Resource Access Across Processes on Unix 2.10 Synchronizing Resource Access Across Processes on Windows 2.11 Creating Files for Temporary Use 2.12 Restricting Filesystem Access on Unix 2.13 Restricting Filesystem and Network Access on FreeBSD 3. Input Validation 3.1 Understanding Basic Data Validation Techniques 3.2 Preventing Attacks on Formatting Functions 3.3 Preventing Buffer Overflows 3.4 Using the SafeStr Library 3.5 Preventing Integer Coercion and Wrap-Around Problems 3.6 Using Environment Variables Securely 3.7 Validating Filenames and Paths 3.8 Evaluating URL Encodings 3.9 Validating Email Addresses 3.10 Preventing Cross-Site Scripting 3.11 Preventing SQL Injection Attacks 3.12 Detecting Illegal UTF-8 Characters 3.13 Preventing File Descriptor Overflows When Using select( ) 4. Symmetric Cryptography Fundamentals 4.1 Representing Keys for Use in Cryptographic Algorithms 4.2 Generating Random Symmetric Keys 4.3 Representing Binary Keys (or Other Raw Data) as Hexadecimal 4.4 Turning ASCII Hex Keys (or Other ASCII Hex Data) into Binary 4.5 Performing Base64 Encoding 4.6 Performing Base64 Decoding 4.7 Representing Keys (or Other Binary Data) as English Text 4.8 Converting Text Keys to Binary Keys 4.9 Using Salts, Nonces, and Initialization Vectors 4.10 Deriving Symmetric Keys from a Password 4.11 Algorithmically Generating Symmetric Keys from One Base Secret 4.12 Encrypting in a Single Reduced Character Set 4.13 Managing Key Material Securely 4.14 Timing Cryptographic Primitives 5. Symmetric Encryption 5.1 Deciding Whether to Use Multiple Encryption Algorithms 5.2 Figuring Out Which Encryption Algorithm Is Best 5.3 Selecting an Appropriate Key Length 5.4 Selecting a Cipher Mode 5.5 Using a Raw Block Cipher 5.6 Using a Generic CBC Mode Implementation 5.7 Using a Generic CFB Mode Implementation 5.8 Using a Generic OFB Mode Implementation 5.9 Using a Generic CTR Mode Implementation 5.10 Using CWC Mode 5.11 Manually Adding and Checking Cipher Padding 5.12 Precomputing Keystream in OFB, CTR, CCM, or CWC Modes (or with Stream Ciphers) 5.13 Parallelizing Encryption and Decryption in Modes That Allow It (Without Breaking Compatibility) 5.14 Parallelizing Encryption and Decryption in Arbitrary Modes (Breaking Compatibility) 5.15 Performing File or Disk Encryption 5.16 Using a High-Level, Error-Resistant Encryption and Decryption API 5.17 Performing Block Cipher Setup (for CBC, CFB, OFB, and ECB Modes) in OpenSSL 5.18 Using Variable Key-Length Ciphers in OpenSSL 5.19 Disabling Cipher Padding in OpenSSL in CBC Mode 5.20 Performing Additional Cipher Setup in OpenSSL 5.21 Querying Cipher Configuration Properties in OpenSSL 5.22 Performing Low-Level Encryption and Decryption with OpenSSL 5.23 Setting Up and Using RC4 5.24 Using One-Time Pads 5.25 Using Symmetric Encryption with Microsoft's CryptoAPI 5.26 Creating a CryptoAPI Key Object from Raw Key Data 5.27 Extracting Raw Key Data from a CryptoAPI Key Object 6. Hashes and Message Authentication 6.1 Understanding the Basics of Hashes and MACs 6.2 Deciding Whether to Support Multiple Message Digests or MACs 6.3 Choosing a Cryptographic Hash Algorithm 6.4 Choosing a Message Authentication Code 6.5 Incrementally Hashing Data 6.6 Hashing a Single String 6.7 Using a Cryptographic Hash 6.8 Using a Nonce to Protect Against Birthday Attacks 6.9 Checking Message Integrity 6.10 Using HMAC 6.11 Using OMAC (a Simple Block Cipher-Based MAC) 6.12 Using HMAC or OMAC with a Nonce 6.13 Using a MAC That's Reasonably Fast in Software and Hardware 6.14 Using a MAC That's Optimized for Software Speed 6.15 Constructing a Hash Function from a Block Cipher 6.16 Using a Block Cipher to Build a Full-Strength Hash Function 6.17 Using Smaller MAC Tags 6.18 Making Encryption and Message Integrity Work Together 6.19 Making Your Own MAC 6.20 Encrypting with a Hash Function 6.21 Securely Authenticating a MAC (Thwarting Capture Replay Attacks) 6.22 Parallelizing MACs 7. Public Key Cryptography 7.1 Determining When to Use Public Key Cryptography 7.2 Selecting a Public Key Algorithm 7.3 Selecting Public Key Sizes 7.4 Manipulating Big Numbers 7.5 Generating a Prime Number (Testing for Primality) 7.6 Generating an RSA Key Pair 7.7 Disentangling the Public and Private Keys in OpenSSL 7.8 Converting Binary Strings to Integers for Use with RSA 7.9 Converting Integers into Binary Strings for Use with RSA 7.10 Performing Raw Encryption with an RSA Public Key 7.11 Performing Raw Decryption Using an RSA Private Key 7.12 Signing Data Using an RSA Private Key 7.13 Verifying Signed Data Using an RSA Public Key 7.14 Securely Signing and Encrypting with RSA 7.15 Using the Digital Signature Algorithm (DSA) 7.16 Representing Public Keys and Certificates in Binary (DER Encoding) 7.17 Representing Keys and Certificates in Plaintext (PEM Encoding) 8. Authentication and Key Exchange 8.1 Choosing an Authentication Method 8.2 Getting User and Group Information on Unix 8.3 Getting User and Group Information on Windows 8.4 Restricting Access Based on Hostname or IP Address 8.5 Generating Random Passwords and Passphrases 8.6 Testing the Strength of Passwords 8.7 Prompting for a Password 8.8 Throttling Failed Authentication Attempts 8.9 Performing Password-Based Authentication with crypt( ) 8.10 Performing Password-Based Authentication with MD5-MCF 8.11 Performing Password-Based Authentication with PBKDF2 8.12 Authenticating with PAM 8.13 Authenticating with Kerberos 8.14 Authenticating with HTTP Cookies 8.15 Performing Password-Based Authentication and Key Exchange 8.16 Performing Authenticated Key Exchange Using RSA 8.17 Using Basic Diffie-Hellman Key Agreement 8.18 Using Diffie-Hellman and DSA Together 8.19 Minimizing the Window of Vulnerability When Authenticating Without a PKI 8.20 Providing Forward Secrecy in a Symmetric System 8.21 Ensuring Forward Secrecy in a Public Key System 8.22 Confirming Requests via Email 9. Networking 9.1 Creating an SSL Client 9.2 Creating an SSL Server 9.3 Using Session Caching to Make SSL Servers More Efficient 9.4 Securing Web Communication on Windows Using the WinInet API 9.5 Enabling SSL without Modifying Source Code 9.6 Using Kerberos Encryption 9.7 Performing Interprocess Communication Using Sockets 9.8 Performing Authentication with Unix Domain Sockets 9.9 Performing Session ID Management 9.10 Securing Database Connections 9.11 Using a Virtual Private Network to Secure Network Connections 9.12 Building an Authenticated Secure Channel Without SSL 10. Public Key Infrastructure 10.1 Understanding Public Key Infrastructure (PKI) 10.2 Obtaining a Certificate 10.3 Using Root Certificates 10.4 Understanding X.509 Certificate erification Methodology 10.5 Performing X.509 Certificate Verification with OpenSSL 10.6 Performing X.509 Certificate Verification with CryptoAPI 10.7 Verifying an SSL Peer's Certificate 10.8 Adding Hostname Checking to Certificate erification 10.9 Using a Whitelist to Verify Certificates 10.10 Obtaining Certificate Revocation Lists with OpenSSL 10.11 Obtaining CRLs with CryptoAPI 10.12 Checking Revocation Status via OCSP with OpenSSL 11. Random Numbers 11.1 Determining What Kind of Random Numbers to Use 11.2 Using a Generic API for Randomness and Entropy 11.3 Using the Standard Unix Randomness Infrastructure 11.4 Using the Standard Windows Randomness Infrastructure 11.5 Using an Application-Level Generator 11.6 Reseeding a Pseudo-Random Number Generator 11.7 Using an Entropy Gathering Daemon-Compatible Solution 11.8 Getting Entropy or Pseudo-Randomness Using EGADS 11.9 Using the OpenSSL Random Number API 11.10 Getting Random Integers 11.11 Getting a Random Integer in a Range 11.12 Getting a Random Floating-Point Value with Uniform Distribution 11.13 Getting Floating-Point Values with Nonuniform Distributions 11.14 Getting a Random Printable ASCII String 11.15 Shuffling Fairly 11.16 Compressing Data with Entropy into a Fixed-Size Seed 11.17 Getting Entropy at Startup 11.18 Statistically Testing Random Numbers 11.19 Performing Entropy Estimation and Management 11.20 Gathering Entropy from the Keyboard 11.21 Gathering Entropy from Mouse Events on Windows 11.22 Gathering Entropy from Thread Timings 11.23 Gathering Entropy from System State 12. Anti-Tampering 12.1 Understanding the Problem of Software Protection 12.2 Detecting Modification 12.3 Obfuscating Code 12.4 Performing Bit and Byte Obfuscation 12.5 Performing Constant Transforms on Variables 12.6 Merging Scalar Variables 12.7 Splitting Variables 12.8 Disguising Boolean Values 12.9 Using Function Pointers 12.10 Restructuring Arrays 12.11 Hiding Strings 12.12 Detecting Debuggers 12.13 Detecting Unix Debuggers 12.14 Detecting Windows Debuggers 12.15 Detecting SoftICE 12.16 Countering Disassembly 12.17 Using Self-Modifying Code 13. Other Topics 13.1 Performing Error Handling 13.2 Erasing Data from Memory Securely 13.3 Preventing Memory from Being Paged to Disk 13.4 Using Variable Arguments Properly 13.5 Performing Proper Signal Handling 13.6 Protecting against Shatter Attacks on Windows 13.7 Guarding Against Spawning Too Many Threads 13.8 Guarding Against Creating Too Many Network Sockets 13.9 Guarding Against Resource Starvation Attacks on Unix 13.10 Guarding Against Resource Starvation Attacks on Windows 13.11 Following Best Practices for Audit Logging Index

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Book SynopsisSince its original introduction in 1997, the Unified Modeling Language has revolutionized software development. Whether you are looking to use UML as a blueprint language, a sketch tool or as a programming language, this book gives you the need to know information on how to apply UML to your project.

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Book SynopsisAdvancements in the technology and availability of data sources have led to the `Big Data'' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems.Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods.Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key reTable of ContentsList of FiguresList of TablesPreface Section I BACKGROUND Introduction1.1 LARGE-SCALE MACHINE LEARNING 1.2 OPTIMIZATION PROBLEMS 1.3 LINEAR CLASSIFICATION1.3.1 Support Vector Machine (SVM) 1.3.2 Logistic Regression 1.3.3 First and Second Order Methods1.3.3.1 First Order Methods 1.3.3.2 Second Order Methods 1.4 STOCHASTIC APPROXIMATION APPROACH 1.5 COORDINATE DESCENT APPROACH 1.6 DATASETS 1.7 ORGANIZATION OF BOOK Optimisation Problem, Solvers, Challenges and Research Directions2.1 INTRODUCTION 2.1.1 Contributions 2.2 LITERATURE 2.3 PROBLEM FORMULATIONS 2.3.1 Hard Margin SVM (1992) 2.3.2 Soft Margin SVM (1995) 2.3.3 One-versus-Rest (1998) 2.3.4 One-versus-One (1999) 2.3.5 Least Squares SVM (1999) 2.3.6 v-SVM (2000) 2.3.7 Smooth SVM (2001) 2.3.8 Proximal SVM (2001) 2.3.9 Crammer Singer SVM (2002) 2.3.10 Ev-SVM (2003) 2.3.11 Twin SVM (2007) 2.3.12 Capped lp-norm SVM (2017) 2.4 PROBLEM SOLVERS 2.4.1 Exact Line Search Method 2.4.2 Backtracking Line Search 2.4.3 Constant Step Size 2.4.4 Lipschitz & Strong Convexity Constants 2.4.5 Trust Region Method 2.4.6 Gradient Descent Method 2.4.7 Newton Method 2.4.8 Gauss-Newton Method 2.4.9 Levenberg-Marquardt Method 2.4.10 Quasi-Newton Method 2.4.11 Subgradient Method 2.4.12 Conjugate Gradient Method 2.4.13 Truncated Newton Method 2.4.14 Proximal Gradient Method 2.4.15 Recent Algorithms 2.5 COMPARATIVE STUDY 2.5.1 Results from Literature 2.5.2 Results from Experimental Study 2.5.2.1 Experimental Setup and Implementation Details 2.5.2.2 Results and Discussions 2.6 CURRENT CHALLENGES AND RESEARCH DIRECTIONS 2.6.1 Big Data Challenge 2.6.2 Areas of Improvement 2.6.2.1 Problem Formulations 2.6.2.2 Problem Solvers 2.6.2.3 Problem Solving Strategies/Approaches 2.6.2.4 Platforms/Frameworks 2.6.3 Research Directions 2.6.3.1 Stochastic Approximation Algorithms 2.6.3.2 Coordinate Descent Algorithms 2.6.3.3 Proximal Algorithms 2.6.3.4 Parallel/Distributed Algorithms 2.6.3.5 Hybrid Algorithms 2.7 CONCLUSION Section II FIRST ORDER METHODSMini-batch and Block-coordinate Approach 3.1 INTRODUCTION 3.1.1 Motivation 3.1.2 Batch Block Optimization Framework (BBOF) 3.1.3 Brief Literature Review 3.1.4 Contributions 3.2 STOCHASTIC AVERAGE ADJUSTED GRADIENT (SAAG) METHODS3.3 ANALYSIS 3.4 NUMERICAL EXPERIMENTS 3.4.1 Experimental setup 3.4.2 Convergence against epochs 3.4.3 Convergence against Time 3.5 CONCLUSION AND FUTURE SCOPE Variance Reduction Methods 4.1 INTRODUCTION 4.1.1 Optimization Problem 4.1.2 Solution Techniques for Optimization Problem 4.1.3 Contributions 4.2 NOTATIONS AND RELATED WORK 4.2.1 Notations 4.2.2 Related Work 4.3 SAAG-I, II AND PROXIMAL EXTENSIONS 4.4 SAAG-III AND IV ALGORITHMS 4.5 ANALYSIS 4.6 EXPERIMENTAL RESULTS 4.6.1 Experimental Setup 4.6.2 Results with Smooth Problem 4.6.3 Results with non-smooth Problem 4.6.4 Mini-batch Block-coordinate versus mini-batch setting 4.6.5 Results with SVM 4.7 CONCLUSION Learning and Data Access 5.1 INTRODUCTION 5.1.1 Optimization Problem 5.1.2 Literature Review 5.1.3 Contributions 5.2 SYSTEMATIC SAMPLING 5.2.1 Definitions 5.2.2 Learning using Systematic Sampling 5.3 ANALYSIS 5.4 EXPERIMENTS 5.4.1 Experimental Setup 5.4.2 Implementation Details 5.4.3 Results 5.5 CONCLUSION Section III SECOND ORDER METHODS Mini-batch Block-coordinate Newton Method 6.1 INTRODUCTION 6.1.1 Contributions 6.2 MBN 6.3 EXPERIMENTS 6.3.1 Experimental Setup 6.3.2 Comparative Study 6.4 CONCLUSION Stochastic Trust Region Inexact Newton Method 7.1 INTRODUCTION 7.1.1 Optimization Problem 7.1.2 Solution Techniques 7.1.3 Contributions 7.2 LITERATURE REVIEW 7.3 TRUST REGION INEXACT NEWTON METHOD 7.3.1 Inexact Newton Method 7.3.2 Trust Region Inexact Newton Method 7.4 STRON 7.4.1 Complexity 7.4.2 Analysis 7.5 EXPERIMENTAL RESULTS 7.5.1 Experimental Setup 7.5.2 Comparative Study 7.5.3 Results with SVM 7.6 EXTENSIONS 7.6.1 PCG Subproblem Solver 17.6.2 Stochastic Variance Reduced Trust Region Inexact Newton Method 7.7 CONCLUSION Section IV CONCLUSIONConclusion and Future Scope 8.1 FUTURE SCOPE 142 Bibliography Index

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Book SynopsisIf you want to become an iOS developer, you have made an excellent choice with this book. Swift holds a significant position in the iOS industry because of the long list of features it serves. It is user-friendly, has great community support, and offers a greater extent of customization. As a result, we can observe a sharp increase in the market demand for developing Apple mobile applications, and with that, companies search for smart developers with the right skill set. Mastering Swift introduces Apple's excellent Swift standard library style and incorporates usage feedback across multiple Swift projects. However, it should be regarded as a living, changeable document and the basis upon which the programming language is implemented.Before going further into the details of the Swift programming language, the book briefly explains the basic information about the language. It is a high-level language created to develop multifaceted iOS applications that cater to d

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Book SynopsisDatabases have become an integral part of modern-day life. We live in an information-driven society and database technology has a direct impact on our daily lives. Decisions are routinely made by organizations based on the information collected and stored in the databases. Because databases play such an important role in business and society, database programming is a key skill.SQL Server Database Programming with C#: Desktop and Web Applications is for college students and software programmers who want to develop practical and commercial skills in database programming with C# or Visual C#.NET 2022 as well as the relational database Microsoft SQL Server 2019. The book explains the practical considerations and applications in database programming with Visual C# 2022 and provides realistic examples and detailed explanations. A direct writing style is combined with real-world examples to provide readers with a clear picture of how to handle database programming issues in the VisTable of ContentsCopyrights and Trademarks. Preface. Acknowledgements. About the Author. Chapter 1 Introduction. Chapter 2 Introduction to Databases. Chapter 3 Introduction to ADO.NET. Chapter 4 Introduction to Language Integrated Query (LINQ). Chapter 5 Data Selection Query with Visual C#.NET. Chapter 6 Data Inserting with Visual C#.NET. Chapter 7 Data Updating and Deleting with Visual C#.NET. Chapter 8 Accessing Data in ASP.NET. Chapter 9 ASP.NET Web Services. Index.

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Book SynopsisUnlock the power of data analytics in finance with this comprehensive guide. Data Analytics for Finance Using Python is your key to unlocking the secrets of the financial markets.In this book, youâll discover how to harness the latest data analytics techniques, including machine learning and inferential statistics, to make informed investment decisions and drive business success. With a focus on practical application, this book takes you on a journey from the basics of data preprocessing and visualization to advanced modeling techniques for stock price prediction.Through real-world case studies and examples, youâll learn how to: Uncover hidden patterns and trends in financial data Build predictive models that drive investment decisions Optimize portfolio performance using data-driven insights Stay ahead of the competition with cutting-edge data analytics techniques Whether youâre a finance professional seeking

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Book SynopsisThis practical guide provides recipes to help you unravel common problems and perform useful tasks when working with Go. Each recipe includes self-contained code solutions that you can freely use, along with a discussion of how and why they work.

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Book Synopsis

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Book SynopsisTable of Contents1. Introduction to Computers, Programs, and Java 2. Elementary Programming 3. Selections 4. Mathematical Functions, Characters, and Strings 5. Loops 6. Methods 7. Single-Dimensional Arrays 8. Multidimensional Arrays 9. Objects and Classes 10. Object-Oriented Thinking 11. Inheritance and Polymorphism 12. Exception Handling and Text I/O 13. Abstract Classes and Interfaces 14. JavaFX Basics 15. Event-Driven Programming and Animations 16. JavaFX UI Controls and Multimedia 17. Binary I/O 18. Recursion Appendixes Appendix A Java Keywords Appendix B The ASCII Character Set Appendix C Operator Precedence Chart Appendix D Java Modifiers Appendix E Special Floating-Point Values Appendix F Number Systems Appendix G Bitwise Operations Appendix H Regular Expressions Appendix I Enumerated Types

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Book Synopsisvi and Vim are immensely powerful tools for anyone working with Unix, Linux, or Mac OS X, but there are far too many commands for anyone to remember. This handy little book puts all of the essential information about vi and Vim at your fingertips, in a format that makes browsing easy.

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Book SynopsisGet started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book.  With this book, you''ll be able to tackle some of today''s real world big data, smart bots, and other complex data problems. You''ll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage.What You''ll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers WhoTable of Contents1. Machine Learning2. Neural Network3. Training of Multi-Layer Neural Network4. Neural Network and Classification5. Deep Learning6. Convolutional Neural Network

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Book SynopsisDeploy, manage, and secure containers and containerized applications on Google Cloud Platform (GCP). This book covers each container service in GCP from the ground up and teaches you how to deploy and manage your containers on each service. You will start by setting up and configuring GCP tools and the tenant environment. You then will store and manage Docker container images with GCP Container Registry (ACR). Next, you will deploy containerized applications with GCP Cloud Run and create an automated CI/CD deployment pipeline using Cloud Build. The book covers GCP''s flagship service, Google Kubernetes Service (GKE), and deployment of a Kubernetes cluster using clear steps and considering GCP best practices using the GCP management console and gcloud command-line tool. Also covered is monitoring containers and containerized applications on GCP with Cloud Monitoring, and backup and restore containers and containerized applications on GCP. By the endTable of ContentsChapter 1: Get Started with Google Cloud Platform (GCP)Chapter Goal: Setup and configure GCP tools and tenant environment No of pages: 40 Sub -Topics1. Set up your Google Cloud Platform (GCP) tenant2. Understanding GCP projects3. Understanding cloud shell4. Secure and manage your GCP account (projects and more)5. GCP Services overview Chapter 2: Store and Manage Docker Container Images with GCP Container Registry (ACR)Chapter Goal: Here we learn how to Store Docker Container images on GCP Container registry No of pages: 40Sub - Topics 1. Setup GCP Container Registry2. Push Docker images to Container Registry3. Pull images from GCP Container Registry4. Manage and secure GCP Container RegistryChapter 3: Deploy Containerized Applications with GCP Cloud RunChapter Goal: This chapter explains how to deploy containers and containerized applications on GCP cloud run No of pages: 40Sub - Topics: 1. Set up GCP cloud run 2. Deploy containers with cloud run3. Use cloud build and git to deploy containers 4. Scale containerized applications on cloud run5. Monitor and manage containerized applications on cloud runChapter 4: Deploy Containerized Applications with Google Kubernetes Engine (GKE)Chapter Goal: This chapters explains how to deploy containers and containerized applications with GKENo of pages:Sub - Topics: 1. Getting started with GKE2. Setup and configure GKE networking and storage3. Deploy Kubernetes dashboard (Web UI) on GKE4. Manage and secure GKE5. Run Batch jobs on Kubernetes with batch (beta)Chapter 5: Deploy Docker Containers on GCP Compute Engine Chapter Goal: This chapter explains how to deploy containers and containerized applications on GCP compute engineNo of pages: 40Sub - Topics: 1. Install Docker container host on Ubuntu Linux VM 2. Install Docker container host on Windows server 2019 VM3. Deploy containers on GCP compute engine using GCP container-optimized OSChapter 6: Secure your GCP Environment and Containers Chapter Goal: This chanpters explains how to secure and protect containers and containerized applications on GCPNo of pages: 40Sub - Topics: 1. Introduction to GCP identify infrastructure 2. Setup organization policies 3. Roles, service accounts and auditing capabilities4. GCP networking and firewalls configuration Chapter 7: Scale Containers and Containerized Applications on GCPChapter Goal: This chapter explains how to scale containers and containerized applications on GCPNo of pages: 40Sub - Topics: 1. Scale Google Kubernetes Service (GKE)2. Scale cloud run and cloud build containers 3. Scale GCP Container Registry4. Scale compute engine hosts and containers Chapter 8: Monitor Containers and Containerized Applications on GCP with Stackdriver MonitoringChapter Goal: Learn how to Monitor Containers and Containerized Applications on GCPNo of pages: 40Sub - Topics: 1. Monitor Google Kubernetes Service (GKE) 2. Monitor cloud run containers 3. Monitor compute engine resources 4. GCP cost management and tools Chapter 9: Backup and Restore Containers and Containerized Applications on GCPChapter Goal: This chapter explains how to backup and restore containers and containerized applications on GCPNo of pages: 40Sub - Topics: 1. Backup persistent storage disks 2. Backup compute engine resources 3. Manage cloud storage and file store Chapter 10: Troubleshooting Containers and Containerized Applications on GCP Chapter Goal: This chapters explains how to troubleshoot containers and containerized applications issues on GCPNo of pages: 40Sub - Topics: 1. Troubleshoot Google Kubernetes Service (GKE)2. Troubleshoot cloud run and cloud build deployments 3. Troubleshoot GCP Container Registry5. Troubleshoot compute engine resource

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Book SynopsisCover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook''s open-source Prophet model, and Amazon''s DeepAR model.Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models. Each of the models presented in this book is covered in depth, with an intuitive simple explanation of thTable of ContentsPART I: Machine Learning for ForecastingChapter 1: Models for ForecastingChapter Goal: Explains the different categories of models that are relevant for forecasting in high level languageNo pages: 10Sub -Topics1. Time series models2. Supervised vs unsupervised models3. Classification vs regression models4. Univariate vs multivariate modelsChapter 2: Model Evaluation for ForecastingChapter Goal: Explains model evaluation with specific adaptations to keep in mind for forecastingNo pages: 15Sub -Topics1. Train test split2. Cross validation for forecasting3. BacktestingPART II: Univariate Time Series ModelsChapter 3: The AR ModelChapter Goal: explain the AR model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding AR model2. Mathematical explanation of the AR model3. Worked out Python forecasting example with the AR modelChapter 4: The MA modelChapter Goal: explain the MA model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding MA model2. Mathematical explanation of the MA model3. Worked out Python forecasting example with the MA modelChapter 5: The ARMA modelChapter Goal: explain the ARMA model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding ARMA model2. Mathematical explanation of the ARMA model3. Worked out Python forecasting example with the ARMA modelChapter 6: The ARIMA modelChapter Goal: Explains the ARIMA model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding ARIMA model2. Mathematical explanation of the ARIMA model3. Worked out Python forecasting example with the ARIMA modelChapter 7: The SARIMA ModelChapter Goal: Explains the SARIMA model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding SARIMA model2. Mathematical explanation of the SARIMA model3. Worked out Python forecasting example with the SARIMA modelPART III: Multivariate Time Series ModelsChapter 8: The VAR modelChapter Goal: Explains the VAR model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding VAR model2. Mathematical explanation of the VAR model3. Worked out Python forecasting example with the VAR modelChapter 9: The Bayesian VAR modelChapter Goal: Explains the Bayesian VAR model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding Bayesian VAR model2. Mathematical explanation of the Bayesian VAR model3. Worked out Python forecasting example with the Bayesian VAR modelPART IV: Supervised Machine Learning ModelsChapter 10: The Linear Regression modelChapter Goal: Explains the Linear Regression model (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding Linear Regression model2. Mathematical explanation of the Linear Regression model3. Worked out Python forecasting example with the Linear Regression modelChapter 11: The Decision Tree modelChapter Goal: Explains the Decision Tree model (intuitively, mathematically and give Python application with code and data set)No pages: 8Sub -Topics1. Understanding Decision Tree model2. Mathematical explanation of the Decision Tree model3. Worked out Python forecasting example with the Decision Tree modelChapter 12: The k-Nearest Neighbors VAR modelChapter Goal: explain the k-Nearest Neighbors (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding k-Nearest neighbors model2. Mathematical explanation of the k-Nearest neighbors model3. Worked out Python forecasting example with the k-Nearest neighbors modelChapter 13: The Random Forest ModelChapter Goal: explain the Random Forest (intuitively, mathematically and give python application with code and data set)No pages: 8Sub -Topics1. Understanding Random Forest model2. Mathematical explanation of the Random Forest model3. Worked out Python forecasting example with the Random Forest modelChapter 14: The XGBoost modelChapter Goal: Explains the XGBoost model (intuitively, mathematically and give python application with code and data set)No pages: 10Sub -Topics1. Understanding XGBoost model2. Mathematical explanation of the XGBoost model3. Worked out Python forecasting example with the XGBoost modelChapter 15: The Neural Network modelChapter Goal: Explains the Neural Network model (intuitively, mathematically and give python application with code and data set)No pages: 10Sub -Topics1. Understanding Neural Network model2. Mathematical explanation of the Neural Network model3. Worked out Python forecasting example with the Neural Network modelPart V: Advanced Machine and Deep Learning ModelsChapter 16: Recurrent Neural NetworksChapter Goal: Explains Recurrent Neural Networks (intuitively, mathematically and give python application with code and data set)No pages: 10Sub -Topics1. Understanding Recurrent Neural Networks2. Mathematical explanation of Recurrent Neural Networks 3. Worked out Python forecasting example with Recurrent Neural Networks Chapter 17: LSTMsChapter Goal: Explains LSTMs (intuitively, mathematically and give python application with code and data set)No pages: 10Sub -Topics1. Understanding LSTMs2. Mathematical explanation of LSTMs 3. Worked out Python forecasting example with LSTMs Chapter 18: Facebook’s Prophet modelChapter Goal: Explains Facebook’s Prophet model (intuitively, mathematically and give Python application with code and data set)No pages: 10Sub -Topics1. Understanding Facebook’s Prophet model2. Mathematical explanation of Facebook’s Prophet model3. Worked out Python forecasting example with Facebook’s Prophet modelChapter 19: Amazon’s DeepAR ModelChapter Goal: Explains Amazon’s DeepAR model (intuitively, mathematically and give python application with code and data set)No pages: 10Sub -Topics1. Understanding Amazon’s DeepAR model2. Mathematical explanation of Amazon’s DeepAR model3. Worked out Python forecasting example with Amazon’s DeepAR modelChapter 20: Deep State Space ModelsChapter Goal: Explains Deep State Space models (intuitively, mathematically and give Python application with code and data set)No pages: 10Sub -Topics1. Understanding Deep State Space models2. Mathematical explanation of Deep State Space models3. Worked out Python forecasting example with Deep State Space modelsChapter 21: Model selectionChapter Goal: Give elements to select the best model for a specific situationNo pages: 16Sub -Topics1. Benchmark scores vs understandability of models vs compute time 2. Black swan outlier problems3. Automated retraining and updating of models4. Conclusion

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Book SynopsisGet started with artificial intelligence for medical sciences and psychology. This book will help healthcare professionals and technologists solve problems using machine learning methods, computer vision, and natural language processing (NLP) techniques. The book covers ways to use neural networks to classify patients with diseases. You will know how to apply computer vision techniques and convolutional neural networks (CNNs) to segment diseases such as cancer (e.g., skin, breast, and brain cancer) and pneumonia. The hidden Markov decision making process is presented to help you identify hidden states of time-dependent data. In addition, it shows how NLP techniques are used in medical records classification. This book is suitable for experienced practitioners in varying medical specialties (neurology, virology, radiology, oncology, and more) who want to learn Python programming to help them work efficiently. It is also intended for data scientists, machine leTable of ContentsChapter 1: An Introduction to Artificial Intelligence for Medical SciencesChapter goal: This is the initial chapter. Subsequently, it encapsulates the specific context and structure of the book. Then, it states the varying medical specialties central to this book. Likewise, it properly presents independent subsets of artificial intelligence. Besides that, it unveils valuable tools for undertaking exercises; Python programming language, distribution package, and libraries. Afterward, it sufficiently acquaints you with different algorithms, including when to carry them out.Sub-topics:● Context of the book.● The book’s central point.● Artificial Intelligence subsets covered in this book.● Structure of the book.● Tools that this book implements.○ Python distribution package.○ Anaconda distribution package.○ Jupyter Notebook.○ Python libraries.● Encapsulating Artificial Intelligence.● Debunking algorithms.● Debunking supervised algorithms.● Debunking unsupervised algorithms.● Debunking Artificial Neural Networks.Chapter 2: Realizing Patterns in Common Diseases with Neural NetworksChapter goal: This chapter purportedly contains the application of artificial neural networks in modelling medical data. It properly instigates deep belief networks to model data and predicts whether a patient suffers from an ordinary disease (i.e., pneumonia and diabetes). Equally, it appraises the networks with fundamental metrics to discern the magnitude to which the networks set apart patients who suffer from the disease from those who do not.Sub-topics:● Classifying patients’ Cardiovascular disease diagnosis outcome data by executing a deepbelief network.● Preprocessing the Cardiovascular disease diagnosis outcome data.● Debunking deep belief networks.o Designing the deep belief network.o Relu Activation function.o Sigmoid activation function.● Training the deep belief network.● Outlining the deep belief networks predictions.● Considering the deep belief network’s performance.● Classifying patients’ diabetes diagnosis outcome data by executing a deep belief network.● Outlining the deep belief networks predictions .● Considering the deep belief network’s performance.● Conclusion.Chapter 3: A Case for COVID-19 Identifying Hidden States and Simulation ResultsChapter goal: This chapter instigates a set of series analysis methods to uniquely discern patterns in the US COVID-19 confirmed cases. To begin with, the Gaussian Hidden Markov Model inherits the series data, models it and identifies the hidden states, including the means and covariance in those states. Subsequently, the Monte Carlo simulation method replicates US COVID-19 confirmed cases across multiple trials, thus providing us with a rich comprehending of the patternChapter content:● Debunking the Hidden Markov Model● Descriptive analysis● Carrying Out the Gaussian Hidden Markov Modelo Considering the Hidden States in US COVID-19 Confirmed Cases with the GaussianHidden Markov Model● Simulating US COVID-19 Confirmed Cases with the Monte Carlo Simulation Methodo US COVID-19 confirmed cases simulation results● ConclusionChapter 4: Cancer Segmentation with Neural NetworksChapter goal: This chapter typically exhibits the practical application of computer vision andconvolutional neural networks for breast and skin Cancer realization and segmentation. Equally, it shows an approach to filter medical scans by applying canny, luplican, and sobel filters. It concludes by ascertaining the extent to which the networks accurately differentiate scans of patients with and without Cancer.Chapter content:● Debunking Cancer.● Debunking Skin Cancer● Depicting scans of a patient with Skin Cancer.● Classifying Patients’ Skin Cancer Diagnosis Image Data by Executing a Convolutional Neural Network.o Preprocessing the training Skin Cancer Image Data.o Preprocessing the Validation Skin Cancer Image Data.o Generating the Training Skin Cancer Diagnosis Image Data.o Tuning the Training Skin Cancer Image Data.o Executing the Convolutional Neural Network to Classify Patients’ Skin CancerDiagnosis Image Data.o Considering the Convolutional Neural Network’s Performance.o Debunking Breast Cancer.● Classifying Ultrasound Scans of Breast Cancer Patients by Executing a Convolutional Neural Network.o Preprocessing the Validation Breast Cancer Image Data .o Preprocessing the Validation Breast Cancer Image Data .o Generating the Training Breast Cancer Diagnosis Image Data.o Tuning the Training Breast Cancer Image Data.o Executing the Convolutional Neural Network to Classify Patients’ Breast CancerDiagnosis Image Data.o Considering the Convolutional Neural Network’s Performance.● Conclusion.Chapter 5: Modelling Magnetic Resonance Imaging and X-Rays by Carrying out Artificial Neural NetworksChapter goal: This chapter intimately acquaints you with the practical application of computer vision and artificial neural networks in neurology and radiology. It promptly carries out convolutional neural networks for image classification. The initial network models MRI scans to set apart patients with and without a brain tumor, and the second network models X-ray scans to set apart patients with and without pneumonia. Besides that, it unveils an effective technique for appraising networks in medical image classification.Sub-topics:● Debunking Brain Tumors.● Classifying Patients’ Model Magnetic Resonance Imaging (MRI) Data by Executing aConvolutional Neural Network.o Depicting MRI Scan of Patients with a Brain Tumor.o Depicting Brain Scans without a Brain Tumor.o Preprocessing the Training MRI Image Data.o Preprocessing the Validation MRI Image Data.o Generating the Training MRI Image Data.o Tuning the Training MRI Image Data.o Executing the Convolutional Neural Network to Classify Patients’ MRI Image Data.o Considering the Convolutional Neural Network’s Performance.● Debunking Pneumonia.o Classifying Patients’ CT scan Data by Executing a Convolutional Neural Network.o Depicting an X-Ray scan of a Patient with Pneumonia.o Depicting an X-Ray scan of a Patient without Pneumonia.o Processing the X-Ray Image Data.o Generating the Training Chest X-Ray Image Data.o Preprocessing the Validation Chest X-Ray Image Data.o Generating the Validation Chest X-Ray Image Data.o Tuning the Training Chest X-Ray Image Data.o Executing the Convolutional Neural Network to Classify Patients’ Chest X-Ray ImageData.▪ Considering the Convolutional Neural Network’s Performance.● Conclusion.Chapter 6: A Case for COVID-19 CT Scan SegmentationChapter goal: This chapter presents an approach for carrying out convolutional neural networks to model chest CT scan images and differentiate between patients with and without COVID-19.Sub-topics:● Classifying Patients’ Model Magnetic Resonance Imaging (MRI) Data by Carrying out aConvolutional Neural Network.o Depicting a Chest CT scan of a COVID-19 Negative Patient.o Depicting a CT scan of COVID-19 Negative Patient.o Preprocessing the Training COVID-19 Data.o Preprocessing the Validation COVID-19 CT Scan Data.o Generating the Training COVID-19 CT Scan Data.o Tuning the Training COVID-19 CT Scan Data.● Data.o Considering the Convolutional Neural Network’s Performance.● Conclusion.Chapter 7 Modelling Clinical Trial DataChapter goal: This chapter familiarizes you with the prime essentials of the most widespread method for adequately investigating data from a clinical trial, recognized as a survival method. It debunks the Nelson-Aalen additive model. To begin with, it encapsulates the method. Subsequently, it promptly presents exploratory analysis, then correlation analysis by carrying out the Pearson correlation method. Following that, it outlines the survival table, then fits the model. It concludes by carefully outlining the profile table, confidence interval, and reproducing the cumulative and baseline hazard.sub-topics:● Debunking Clinical Trials.● An Overview of Survival Analysis.● Context of the Chapter.● Exploring the Nelson-Aalen Additive Model.● Descriptive Analysis.● Realizing a Correlation Relationship.● Outlining the Survival Table.● Carrying out the Nelson-Aalen Additive Model.o Outlining the Nelson-Aalen additive Model’s Confidence Intervalo Discerning the Survival Hazard.o Discerning the Cumulative Survival Hazard.o Baseline Survival Hazard.● Conclusion.● References.Chapter 8: Medical Record CategorizationChapter goal: This chapter sufficiently apprises a wholesome approach for realizing patterns in medical records by carrying out a linear discriminant analysis model. To begin with, it summarizes medical recording. Subsequently, it exhibits a technique of cleansing textual data by carrying out fundamental methods like regularization and TfidfVectorizer. Afterward, it executes the method to classify the medical specialty, then it assesses the extent to which it segregates classes.Sub-topics:● Medical Records.● Context of Chapter.● Debunking Categorization with Linear Discriminant Analysis.o Descriptive Statistics.o Preprocessing the Medical Records Data.o Carrying out Regular Expression.o Carrying Out Word Vectorization.o Carrying out the Linear Discriminant Analysis Model to Classify Patients’ MedicalRecords.o Considering the Linear Discriminant Analysis Model’s Performance.● Conclusion.Chapter 9: A Case for Psychology: Factoring and Clustering Personality DimensionsChapter goal: This chapter introduces you to analyzing the underlying patterns in human behavior by promptly carrying out exploratory factor analysis and cluster analysis. To begin with, it covers the big five personality dimensions. Following that, it presents an approach for typically collecting data by retaining a Likert scale and measuring the reliability of the scale with Cronbach’s reliability testing strategy. Subsequently, it performs factor analysis; beginning with estimating Bartlett Sphericity statistics, then the Kaiser-Meyer-Olkin statistic. Following that, it rotates the eigenvalues by carrying out the varimax rotation method and estimates the proportional variances and cumulative variances. In addition, it executes the K-Means method to observe clusters in the data; beginning with standardizing the data and carrying out principal component analysis.Sub-topics:● Debunking Personality Dimensions.● Questionnaires.● Likert Scale.● Reliability.o Spearman-Brown Reliability Testing Strategy.o Carrying out Cronbach's Reliability Testing Strategy.● Carrying out Factor Model.o Carrying out the Bartlett Sphericity Test.o Carrying out the Kaiser-Meyer-Olkin Test.o Discerning K with a Scree Plot.o Carrying out Eigenvalue Rotation.▪ Varimax Rotation.● Carrying out Cluster Analysis.o Carrying out Principal Component Analysis.O Returning K-Means label.

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Book SynopsisThis in-depth Spring-based Java developer code reference has been updated and now solves many of your Spring Framework 6 problems using reusable, complete and real-world working code examples. Spring 6 Recipes (5th Edition) now includes Spring Native which speeds up your Java-based Spring Framework built enterprise, native cloud, web applications and microservices. It also has been updated to now include Spring R2DBC for Reactive Relational Database Connectivity, a specification to integrate SQL databases, like PostgreSQL, MySQL and more, using reactive drivers.Furthermore, this book includes additional coverage of WebFlux for more reactive Spring web applications.  Reactive programming allows you to build systems that are resilient to high load, especially common in the more complex enterprise, native cloud applications that Spring Framework lets you build. This updated edition also uses code snippets and examples based on neTable of Contents1. Spring Development Tools2. Spring Core Tasks3. Spring Native: Spring + GraalVM4. Spring MVC5. Spring REST6. Spring MVC - Async Processing7. Spring WebFlux8. Spring Security9. Data Access10. Spring R2DBC11. Spring Transaction Management12. Spring Batch13. Spring with NoSQL14. Spring Java Enterprise Services and Remoting Technologies15. Spring Messaging16. Spring Integration17. Spring TestingA. Spring Deployment to the CloudB. Caching.

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Book SynopsisOverhaul your debugging techniques and master the theory and tools needed to debug and troubleshoot cloud applications in production environments. This book teaches debugging skills that universities often avoid, but that typically consume as much as 60% of our time as developers. The book covers the use of debugger features such as tracepoints, object marking, watch renderers, and more. Author Shai Almog presents a scientific approach to debugging that is grounded in theory while being practical enough to help you to chase stubborn bugs through the maze of a Kubernetes deployment. Practical Debugging at Scale assumes a polyglot environment as is common for most enterprises, but focuses on JVM environments. Most of the tooling and techniques described are applicable to Python, Node, and other platforms, as well as to Java and other JVM languages. The book specifically covers debugging in production, an often-neglected discipline but an all too painful reaTable of ContentsIntroductionPart I. Basics1. Know Your Debugger2. The Checklist3. The Auxiliary Tools4. Logging, Testing, and Fail Fast5. Time Travel DebuggingPart II. The Modern Production Environment6. Debugging Kubernetes7. Serverless Debugging8. Fullstack Debugging9. Observability and Monitoring10. Developer ObservabilityPart III. In Practice11. Tools of Learning12. Performance and Memory13. Security14. Bug Strategies

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Book SynopsisIn this thoroughly revised edition, author and gawk lead developer Arnold Robbins describes the awk language and gawk program in detail, shows you how to use awk and gawk for problem solving, and then dives into specific features of gawk.

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Book SynopsisThis book will demystify Angular as a framework, as well as provide clear instructions and examples on how to get started with writing scalable Angular applications.

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Book SynopsisIn Think Complexity, you'll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics. Whether you're an intermediate-level Python programmer or a student of computational modeling, you'll delve into examples of complex systems through a series of worked examples, exercises and case studies.

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Book SynopsisDescription Xamarin is a toolset that allows users to write native mobile apps in C# and run them on both iOS and Android devices. Xamarin in Action teaches readers how to build Xamarin apps on iOS and Android from scratch while maximizing code re-use. By the end, readers will be able to build a high quality production-ready Xamarin app on iOS and Android from scratch with a high level of code reuse. Key Features • Layer-by-layer guide • Hands-on lessons • Teaches you to build production ready apps Audience This book is for C# developers with a few months through many years of experience who want to build native mobile apps for iOS and Android using the language and toolset they already know. About the technology Xamarin gives users the ability to share large portions of code across two platforms while still letting them write native apps that can take full advantage of the device and OS features specific to each platform. Jim Bennett is a Xamarin MVP, Microsoft MVP, Xamarin Certified Developer and an active community member. He's also a frequent speaker at events all around the world, including Xamarin user groups and Xamarin and Microsoft conferences. He regularly blogs about Xamarin development at https://jimbobbennett.io.

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Book SynopsisObject Design Style Guide captures dozens of techniques for creating pro-quality OO code that can stand the test of time. Examples are in an instantly-familiar pseudocode, teaching techniques you can apply to any OO language, from C++ to PHP. The design rules for different types of objectsBest practices for naming objectsTesting an object’s behavior instead of its implementationExercises for each chapter to test your design skills

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Book SynopsisYour customers want rock-solid, bug-free software that does exactly what they expect it to do. Yet they can't always articulate their ideas clearly enough for you to turn them into code. You need Cucumber: a testing, communication, and requirements tool-all rolled into one. All the code in this book is updated for Cucumber 2.4, Rails 5, and RSpec 3.5. Express your customers' wild ideas as a set of clear, executable specifications that everyone on the team can read. Feed those examples into Cucumber and let it guide your development. Build just the right code to keep your customers happy. You can use Cucumber to test almost any system or any platform. Get started by using the core features of Cucumber and working with Cucumber's Gherkin DSL to describe-in plain language-the behavior your customers want from the system. Then write Ruby code that interprets those plain-language specifications and checks them against your application. Next, consolidate the knowledge you've gained with a worked example, where you'll learn more advanced Cucumber techniques, test asynchronous systems, and test systems that use a database. Recipes highlight some of the most difficult and commonly seen situations the authors have helped teams solve. With these patterns and techniques, test Ajax-heavy web applications with Capybara and Selenium, REST web services, Ruby on Rails applications, command-line applications, legacy applications, and more. Written by the creator of Cucumber and the co-founders of Cucumber Ltd., this authoritative guide will give you and your team all the knowledge you need to start using Cucumber with confidence. What You Need: Windows, Mac OS X (with XCode) or Linux, Ruby 1.9.2 and upwards, Cucumber 2.4, Rails 5, and RSpec 3.5

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Book SynopsisGive users the real-time experience they expect, by using Elixir and Phoenix Channels to build applications that instantly react to changes and reflect the application's true state. Learn how Elixir and Phoenix make it easy and enjoyable to create real-time applications that scale to a large number of users. Apply system design and development best practices to create applications that are easy to maintain. Gain confidence by learning how to break your applications before your users do. Deploy applications with minimized resource use and maximized performance. Real-time applications come with real challenges - persistent connections, multi-server deployment, and strict performance requirements are just a few. Don't try to solve these challenges by yourself - use a framework that handles them for you. Elixir and Phoenix Channels provide a solid foundation on which to build stable and scalable real-time applications. Build applications that thrive for years to come with the best-practices found in this book. Understand the magic of real-time communication by inspecting the WebSocket protocol in action. Avoid performance pitfalls early in the development lifecycle with a catalog of common problems and their solutions. Leverage GenStage to build a data pipeline that improves scalability. Break your application before your users do and confidently deploy them. Build a real-world project using solid application design and testing practices that help make future changes a breeze. Create distributed apps that can scale to many users with tools like Phoenix Tracker. Deploy and monitor your application with confidence and reduce outages. Deliver an exceptional real-time experience to your users, with easy maintenance, reduced operational costs, and maximized performance, using Elixir and Phoenix Channels. What You Need: You'll need Elixir 1.9+ and Erlang/OTP 22+ installed on a Mac OS X, Linux, or Windows machine.

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Book SynopsisStart building native Android apps the modern way in Kotlin with Jetpack's expansive set of tools, libraries, and best practices. Learn how to create efficient, resilient views with Fragments and share data between the views with ViewModels. Use Room to persist valuable data quickly, and avoid NullPointerExceptions and Java's verbose expressions with Kotlin. You can even handle asynchronous web service calls elegantly with Kotlin coroutines. Achieve all of this and much more while building two full-featured apps, following detailed, step-by-step instructions. With Kotlin and Jetpack, Android development is now smoother and more enjoyable than ever before. Dive right in by developing two complete Android apps. With the first app, Penny Drop, you create a full game complete with random die rolls, customizable rules, and AI opponents. Build lightweight Fragment views with data binding, quickly and safely update data with ViewModel classes, and handle all app navigation in a single location. Use Kotlin with Android-specific Kotlin extensions to efficiently write null-safe code without all the normal boilerplate required for pre-Jetpack + Kotlin apps. Persist and retrieve data as full objects with the Room library, then display that data with ViewModels and list records in a RecyclerView. Next, you create the official app for the Android Baseball League. It's a fake league but a real app, where you use what you learn in Penny Drop and build up from there. Navigate all over the app via a Navigation Drawer, including specific locations via Android App Links. Handle asynchronous and web service calls with Kotlin Coroutines, display that data smoothly with the Paging library, and send notifications to a user's phone from your app. Come build Android apps the modern way with Kotlin and Jetpack! What You Need: You'll need the Android SDK, a text editor, and either a real Android device or emulator for testing. While not strictly required, it's assumed you're using Android Studio, which comes with the Android SDK and simplifies creating an emulator. Also, a few examples require JDK 1.8 or later, though all of these pieces can be completed in other ways when using JDK 1.6.

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Book SynopsisDiscover easy ways to control your home with the powerful new Raspberry Pi hardware. Program short Python scripts that will detect changes in your home and react with the instructions you code. Use new add-on accessories to monitor a variety of measurements, from light intensity and temperature to motion detection and water leakage. Expand the base projects with your own custom additions to perfectly match your own home setup. Most projects in the book can be completed in under an hour, giving you more time to enjoy and tweak your autonomous creations. No breadboard or electronics knowledge required! Get to know the latest Raspberry Pi hardware, and create awesome automation solutions for home or work that don't require an electronics degree, cumbersome add-ons, or expensive third-party subscription services. Create easy to run Python scripts on your own that make your Pi do things that would have required a team of automation experts to build only a few years ago. Connect to and control popular home automation lighting systems from a Raspberry Pi. Trigger autonomous actions based on movement, temperature, and timer events. Power on your own computer and appliances using your voice. Remotely control infrared-enabled consumer electronics, create chatbots to retrieve personalized items of interest, and implement a temperature-monitoring room fan. These are just some of the projects that the book will show you how to make. Most projects can be completed and operational in under an hour, and do not require any messy schematics or a spaghetti bowl of wires and breadboard-attached circuits to operate. Control your home or office exactly the way you want instead of relying on an expensive mysterious box of third-party technology to do it for you. What You Need: Raspberry Pi (Pi 4 Model B or higher recommended) running Raspberry Pi OS

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Book Synopsis

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Book Synopsis

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Book SynopsisSoftware development is becoming recognised more and more as an essential skill and profession in today's increasingly digital world. Familiarity with basic programming concepts is no longer sufficient to succeed as a software developer, today's developers require a wider field of expertise and a holistic, customer-focused approach. This book is a pragmatic guide to software development in practice. It explores the inner workings of software development in the context of the industry, covering good practice for software developers and providing you with tools and practical understanding you'll need to advance within the software development world.Trade ReviewA remarkable book that provides a unique perspective on modern software development. A distinctive and unusual feature is the way modern software development principles are explained holistically in terms of all project activities. The focus on key employment skills and knowledge also makes it a must read for aspiring developers. -- Chris Beaumont PhD FBCS FHEA * Chair of Examiners, NCC Education *I wish this book was around when I was starting out 30 years ago. It’s a manual for all aspects of software development and the scope of the role in business, rather than focusing on being a ‘coder’. I particularly like the fact it includes client aspects, which are usually forgotten! -- Andy Doyle * Director, Nice Group (SW) Ltd *As a leader of many software development teams, this book will be indispensable to modern developers and managers alike. It will not teach you how to write .NET, but will help when someone who does tries to bamboozle you with jargon. It is brilliantly written and easy to digest. -- Paul Leonard * Group Technology & Infrastructure Manager, DCC plc *A comprehensive, practical overview of what awaits you in the real world of professional software development. -- Karl Beecher * Author of Computational Thinking *Software Development in Practice takes the guesswork out of your journey into tech. From term definitions, Agile practices and clean code tips, this book is my go-to resource for anyone breaking into the tech industry. I especially appreciate the emphasis on communication, collaboration and user experience. -- Sjoukje Ijlstra * Software Engineer, JP Morgan *There are many books which describe various technical and theoretical aspects of software development. However, few describe what’s actually involved in day-to-day software development. This book is one of those few and should be of real interest to prospective and early-career software developers. -- Dr Patrick Hill * R&D Director, QPC Ltd *As a security researcher and advocate for embedding security in the software development process, it is enlightening to see this book dedicate some detailed coverage to consider use of defensive coding techniques, GDPR from a developers point of view and looking at specific vulnerabilities and associated mitigations taken direct from the OWASP Top10. -- Adrian Winckles * Director of Cyber and Networking, Anglia Ruskin University *A great book for both those thinking of working or progressing in the commercial software development industry. The book gives insight into working practices, identifying positives and negatives to each of them. Deliberately avoiding specific programming languages (other than to explain some points), the book will be a perfect addition for any dev team in any development environment. -- Martin Thorne * Technical Director, Montpellier Integrated *This book provides the framework to apply knowledge of how to code into the real world of being a software developer. It is the theory and thought processes that you can’t learn without doing the job first - until now! If you’re considering a career path in software development this book should be the first port of call on your journey. -- Kieran Purdie * Pro AV Channel Manager & Business Development / Technical Manager, NETGEAR Business, UK & Ireland *If you want a guide on what you need to do to become a fantastic software developer, then this book is for you. The book’s in-depth topic coverage will provide you with all the tools and information you will need to succeed in the software development Industry. -- Anthony Davis * Senior Manager Platform Engineering, Sixt *IT now permeates almost every area of business, in an environment where the pace is ever increasing it is essential for those aspiring to work as a software developer to gain knowledge, skills and experience in many areas. Software Development in Practice covers the areas to master to become a productive member of a software development team. -- Chris Galley FBCS CITPTable of Contents GETTING STARTED IN SOFTWARE DEVELOPMENT TARGET ROLES OVERVIEW OF DIFFERENT TASKS A COMMERCIAL DEVELOPER MIGHT ENCOUNTER IN THE ROLE OVERVIEW OF SOFTWARE DEVELOPMENT METHODOLOGIES OVERVIEW OF COMMERCIAL SOFTWARE LANGUAGES AND PARADIGMS ANALYSIS AND PLANNING WRITING GOOD-QUALITY CODE DEVELOPING EFFECTIVE USER INTERFACES LINKING PROGRAM CODE TO BACK-END DATA SOURCES TESTING CODE AND ANALYSING RESULTS WORKING WITH STRUCTURED TECHNIQUES TO PROBLEM-SOLVE AND DESIGN SOLUTIONS HOW TO DEBUG CODE AND UNDERSTAND UNDERLYING PROGRAM STRUCTURE WORKING WITH SYSTEMS ANALYSIS ARTEFACTS BUILDING, MANAGING AND DEPLOYING CODE INTO ENTERPRISE ENVIRONMENTS INDUSTRY APPROACHES TO TESTING CLIENT AND STAKEHOLDER FOCUS PROFESSIONAL RECOGNITION FINAL THOUGHTS

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Book SynopsisTake tiny steps to enter the big world of data science through this interesting guideAbout This Book Learn the fundamentals of machine learning and build your own intelligent applications Master the art of building your own machine learning systems with this example-based practical guide Work with important classification and regression algorithms and other machine learning techniquesWho This Book Is ForThis book is for anyone interested in entering the data science stream with machine learning. Basic familiarity with Python is assumed. What You Will Learn Exploit the power of Python to handle data extraction, manipulation, and exploration techniques Use Python to visualize data spread across multiple dimensions and extract useful features Dive deep into the world of analytics to predict situations correctly Implement machine learning classification and regression algorithms from scratch in Python Be amazed to see the algorithms in action Evaluate the performance of a machine learning model and optimize it Solve interesting real-world problems using machine learning and Python as the journey unfoldsIn DetailData science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal. Style and approachThis book is an enticing journey that starts from the very basics and gradually picks up pace as the story unfolds. Each concept is first succinctly defined in the larger context of things, followed by a detailed explanation of their application. Every concept is explained with the help of a project that solves a real-world problem, and involves hands-on workgiving you a deep insight into the world of machine learning. With simple yet rich languagePythonyou will understand and be able to implement the examples with ease.

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