Artificial intelligence (AI) Books
Apress AI for Robotics
Book SynopsisChapter 1: Introduction: General Purpose Robotics.- Chapter 2: Robot Perception: Sensors and Image Processing.- Chapter 3: Robot Perception: 3D Data and Sensor Fusion.- Chapter 4: Foundation Models in Robotics.- Chapter 5: Simulation.- Chapter 6: Mapping, Localization, and Navigation.- Chapter 7: Reinforcement Learning and Control.- Chapter 8: Self Driving Cars.- Chapter 9: Industrial Robotics.- Chapter 10: Humanoid Robotics.- Chapter 11: Data-Driven Robotics in Practice.
£41.24
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Handson Deep Learning
Book SynopsisThis book discusses deep learning, from its fundamental principles to its practical applications, with hands-on exercises and coding. It focuses on deep learning techniques and shows how to apply them across a wide range of practical scenarios. The book begins with an introduction to the core concepts of deep learning. It delves into topics such as transfer learning, multi-task learning, and end-to-end learning, providing insights into various deep learning models and their real-world applications. Next, it covers neural networks, progressing from single-layer perceptrons to multi-layer perceptrons, and solving the complexities of backpropagation and gradient descent. It explains optimizing model performance through effective techniques, addressing key considerations such as hyperparameters, bias, variance, and data division. It also covers convolutional neural networks (CNNs) through two comprehensive chapters, covering the architecture, components, and significance of kernels implementing well-known CNN models such as AlexNet and LeNet. It concludes with exploring autoencoders and generative models such as Hopfield Networks and Boltzmann Machines, applying these techniques to a diverse set of practical applications. These applications include image classification, object detection, sentiment analysis, COVID-19 detection, and ChatGPT. By the end of this book, you will have gained a thorough understanding of deep learning, from its fundamental principles to its innovative applications, enabling you to apply this knowledge to solve a wide range of real-world problems. What You Will LearnWhat are deep neural networks?What is transfer learning, multi-task learning, and end-to-end learning?What are hyperparameters, bias, variance, and data division?What are CNN and RNN?Who This Book Is ForMachine learning engineers, data scientists, AI practitioners, software developers, and engineers interested in deep learning
£46.74
Apress Essential Data Analytics Data Science and AI
Book SynopsisChapter 1: Introduction.- Chapter 2: Obtaining Data.- Chapter 3: ETL Pipeline.- Chapter 4: Exploratory Data Analysis.- Chapter 5: Machine Learning Models.- Chapter 6: Evaluating Models.- Chapter 7: When To Use Machine Learning Models.- Chapter 8: Where Machine Learning Models Live.- Chapter 9: Telemetry.- Chapter 10: Adversaries and Abuse.- Chapter 11: Working With Models.
£39.99
Apress Natural Language Processing on Oracle Cloud Infrastructure
Book SynopsisPart 1: Foundations and Case Study Introduction.- Chapter 1: NLP Essentials.- Chapter 2: Oracle Cloud for NLP.- Chapter 3: Healthcare NLP Case Study.- Part2: Case Study Implementation.- Chapter 4: Tenancy Preparation.- Chapter 5: Dataset Preparation.- Chapter 6: Model Fine-tuning.- Part 3: Case Study Deployment and Wrap-Up.- Chapter 7: Model Deployment and Monitoring.- Chapter 8: MLOps and Conclusion.
£46.74
Apress The Definitive Guide to Machine Learning Operations in AWS
Book SynopsisChapter 1: Introduction to MLOps.- Chapter 2: Foundations of MLOps on AWS.- Chapter 3: Operational Excellence in MLOps.- Chapter 4: Security in MLOps.- Chapter 5: Reliability in MLOps.- Chapter 6: Performance Efficiency in MLOps.- Chapter 7: Cost Optimization in MLOps.- Chapter 8 MLOps Best Practices and Case Studies.- Chapter 9: MLOps for GenAI.- Chapter 10: Future Trends in MLOps.
£52.24
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Building Generative AI Agents
Book Synopsis
£39.99
Apress Responsible AI in Practice
Book SynopsisPart I: Introduction.- Chapter 1: Responsible AI and AI Governance.- Part II: Technical risks (Internal to an organisation).- Chapter 2. Accuracy.- Chapter 3. Robustness and Security.- Chapter 4: Explainability.- Part III: Ethical risks (External).- Chapter 5. Fairness and Human Rights.- Chapter 6: Privacy.- Chapter 7: Sustainability.- Chapter 8: Human-Centered AI.- Part IV: Governance and Case studies.- Chapter 9: Governance Processes.- Chapters 10: Case Study.
£43.99
£42.49
Apress A Practical Guide for Mastering Generative AI Applications Using Amazon Bedrock
Book SynopsisChapter 1: Introduction to Generative AI.- Chapter 2: Generative AI with AWS.- Chapter 3: Introduction to Amazon Bedrock.- Chapter 4: Overview of Prompt Engineering and In-Context Learning.- Chapter 5: Overview of Use Cases in this Book.- Chapter 6: Overview of Retrieval-Augmented Generation (RAG).- Chapter 7: Overview of Amazon Bedrock Knowledge Base.- Chapter 8: Overview of Safeguard's Practice.- Chapter 9: Overview of Amazon Bedrock Agents.- Chapter 10: Overview of Model Customization.- Chapter 11: Overview of Model Evaluation.- Chapter 12: Overview of Best Model Selection and Best Practices.- Chapter 13: Overview of Security and Privacy of Amazon Bedrock.- Chapter 14: Overview of GenAIOps.- Chapter 15: Overview of Prompt Management.- Chapter 16: Overview of Prompt Flow. – Chapter 17: Overview of Provisional Throughput. – Chapter 18: Overview of Image Capabilities. – Chapter 19: Overview of Multimodal Capabilities. – Chapter 20: Conclusion.
£39.59
Apress Conceptual Variable Design for Scorecards
Book SynopsisChapter 1: Conceptual Representations.- Chapter 2: Conceptual Modelling.- Chapter 3: Balance Equation.- Chapter 4: Ratios.- Chapter 5: Time and Behavioral Patterns.-Chapter 6: Additional Variables.- Chapter 7: Things to Know About ABTs.- Chapter 8 The Building Plan and Variable Management.- Chapter 9: Target Population.- Chapter 10: The ABT Building Process.- Chapter 11: A Brief Introduction to the use of SAS® Enterprise MinerTM.- Chapter 12: Partitioning.- Chapter 13: Univariable Analysis.- Chapter 14: Collinearity Analysis.- Chapter 15: Weight of Evidence.- Chapter 16: Multivariable Selection Methods.- Chapter 17: Experimental Design and Hyperoptimization.- Chapter 18: The Main-Effects Model.- Chapter 19: The Scoring Process.- Chapter 20: Closing Thoughts.
£55.24
Apress Intermediate Python and Large Language Models
Book SynopsisChapter 1: LangChain and Python: Basics.- Chapter 2: LangChain and Python: Adanced Components.- Chapter 3: Building Advanced Applications Powered by LLMs with LangChain and Python.- Chapter 4: Deploying LLM-powered Applications.- Chapter 5: Building and Fine-tuning LLMs.
£39.99
Apress Mastering Text Analytics
Book SynopsisChapter 1. Natural Language Processing: An Introduction.- Chapter 2. Collecting and Extracting the Data for NLP Projects.- Chapter 3. NLP Data Preprocessing Tasks Involving Strings & Python Regular Expressions.- Chapter 4. NLP Data Preprocessing Tasks with nltk.- Chapter 5. Lexical Analysis.- Chapter 6. Syntactic and Semantic Techniques in NLP.- Chapter 7. Advanced Pragmatic Techniques and Specialized Topics in NLP.- Chapter 8. Transformers, Generative AI, & LangChain.- Chapter 9. Advancing with LangChain & OpenAI.- Chapter 10. Case Study on Symantec Analysis.
£43.99
Apress Generative AIDriven Application Development with Java
Book Synopsis1. Megabrains 101: Generative AI & LLMs Unboxed.- 2. First Contact: “Hello, LLM” with Spring Boot.- 3. The Transformer Saga—From Attention to Fine-Tuning.- 4. Bring Your Own Model: Self-Hosting with Ollama.- 5. Power Tools: LangChain4j Quick-Start.- 6. Integrating LLMs with Java Applications.- 7. From Chatty to Clever: Retrieval-Augmented Generation.- 8. Spring AI Ninja Moves.- 9. Prompt Alchemy: Patterns that Make Models Look Smarter.- 10. Swiss-Army LLMs: Tool Calls in Spring AI.- 11. Agents Assemble! Building Autonomous Workflows.- 12. Quarkus + LangChain4j: Lightning-Fast Gen AI.- 13. Jlama & Friends: Hosting Models the Java Way.- 14. Seeing Is Believing: Multimodal LLMs & Image Hacking.- 15. Does It Even Work? Testing & Evaluating LLM Apps.- 16. Cloud Power-Ups—Bedrock, Vertex & Azure OpenAI.- 17. Talking in Protocols: The MCP Revolution.- 18. Can You See Me Now? Observability for LLM Pipelines.- 19. Native-Speed Machine Learning in Java: DJL, ONNX & JNI.- 20. Architectures of Tomorrow: From Monoliths to Modular Minds.
£41.24
Apress NoOps
£39.99
Apress Large Language Models Ops for Finance
Book SynopsisChapter 1: Introduction to Large Language Models in Finance.- Chapter 2: Infrastructure Setup for LLMs.- Chapter 3: Training and Fine-Tuning LLMs.- Chapter 4: Deployment Strategies for LLMs.- Chapter 5: Ensuring Data Privacy and Security.- Chapter 6: Integrating LLMs into Financial Systems.- Chapter 7: Monitoring and Maintenance of LLMs.- Chapter 8: Future Trends in LLM Ops for Finance.
£39.99
Apress Mastering Langchain
Book SynopsisChapter 1: Introduction to LangChain.- Chapter 2: Core Components of LangChain.- Chapter 3: Advanced Components and Integrations.- Chapter 4: Building Chatbots Using LangChain.- Chapter 5: Building Retrieval-Augmented Generation (RAG) Systems with LangChain.- hapter 6: Advanced Techniques with LangServe and LangSmith.- Chapter 7: LangChain and NLP: Enhancing Language Understanding.- Chapter 8: Building AI Agents with LangChain.- Chapter 9: LangChain Framework Integrations.- Chapter 10: Deploying LangChain Applications.- Chapter 11: Best Practices and Practical Aspects.
£39.99
Apress Generative AI in R
Book Synopsis1. Introduction to Generative AI and R.- 2. Setting up your R Environment for Generative AI.- 3. Fundamentals of Generative AI .- 4. Implementing Basic Generative Models in R.- 5. Generating Synthetic Data with R.- 6. Advanced Generative Models and Techniques.- 7. Generative AI for Predictive Modeling.- 8. Creative Applications of Generative AI in R.- 9. Ethical Considerations and Future Directions.
£41.24
Apress AIDriven Software Testing
£43.99
Apress Advanced Forecasting with Python
Book SynopsisPART I: Machine Learning for Forecasting.- Chapter 1: Models for Forecasting.- Chapter 2: Model Evaluation for Forecasting.- Chapter 3: Model Management and Benchmarking using MLflow.- PART II: Univariate Time Series Models.- Chapter 4: The AR model.- Chapter 5: The MA model.- Chapter 6: The ARMA model.- Chapter 7: The ARIMA model.- Chapter 8: The SARIMA model.- PART III: Multivariate Time Series Models.- Chapter 9: The SARIMAX model.- Chapter 10: The VAR model.- Chapter 11: The VARMAX model.- PART IV: Supervised Models.- Chapter 12: The Linear Regression.- Chapter 13: The Decision Tree Model.- Chapter 14: The kNN model.- Chapter 15: The Random Forest.- Chapter 16: Gradient Boosting with XGBoost, LightGBM, and CatBoost.- Chapter 17: Bayesian Models with pyBATS.- PART V: Neural Networks.- Chapter 18: Neural Networks.- Chapter 19: RNNs using SimpleRNN and GRU.- Chapter 20: LSTM RNNs.- PART VI: Black Box and Cloud Based Models.- Chapter 21: The NBEATS model with Darts.- Chapter 22: The Transformer model with Darts.- Chapter 23: The NeuralProphet model.- Chapter 24: The DeepAR model and AWS Sagemaker AI.- Chapter 25: Uber's Orbit Model.- Chapter 26: AutoML with Microsoft Azure.- Chapter 27: AutoML with Vertex AI on Google Cloud Platform.- Chapter 28: Nixtla Suite and TimeGPT.- Chapter 29: Model Selection.
£37.49
