{"product_id":"distributed-machine-learning-and-gradient-optimization-9789811634192","title":"Distributed Machine Learning and Gradient Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol.\u003c\/p\u003e  \u003cp\u003eWritten in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal to a broad audience in the field of machine learning, artificial intelligence, big data and database management.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eChapter 1: Introduction\u003cp\u003e1.1.  Background\u003c\/p\u003e  \u003cp\u003e1.2.  Distributed machine learning\u003c\/p\u003e  \u003cp\u003e1.3.  Gradient optimization\u003c\/p\u003e  \u003cp\u003e1.4.  Challenges\u003c\/p\u003e  \u003cp\u003eChapter 2: The preliminaries\u003c\/p\u003e  \u003cp\u003e        2.1. Overview\u003c\/p\u003e  \u003cp\u003e        2.2. Parallel strategy\u003c\/p\u003e  \u003cp\u003e        2.3. Gradient compression\u003c\/p\u003e  \u003cp\u003e        2.4. Synchronization protocol\u003c\/p\u003e  \u003cp\u003eChapter 3: Parallel strategy\u003c\/p\u003e  \u003cp\u003e1.1.  Background and problem\u003c\/p\u003e  \u003cp\u003e1.2.  Data parallelism\u003c\/p\u003e  \u003cp\u003e1.3.  Model parallelism\u003c\/p\u003e  \u003cp\u003e1.4.  Hybrid parallelism\u003c\/p\u003e  \u003cp\u003e        3.5. Benchmark\u003c\/p\u003e  \u003cp\u003e        3.6. Summary\u003c\/p\u003e  Chapter 4: Gradient compression\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e        4.1. Background and problem\u003c\/p\u003e  \u003cp\u003e        4.2. Lossless gradient compression\u003c\/p\u003e  \u003cp\u003e        4.3. Lossy gradient compression\u003c\/p\u003e  \u003cp\u003e        4.4. Sparse gradient compression\u003c\/p\u003e  \u003cp\u003e        4.5. Benchmark\u003c\/p\u003e  \u003cp\u003e        4.6. Summary\u003c\/p\u003e  \u003cp\u003eChapter 5: Synchronization protocol\u003c\/p\u003e  \u003cp\u003e        5.1. Background and problem\u003c\/p\u003e  \u003cp\u003e        5.2. Bulk synchronous protocol\u003c\/p\u003e          5.3. Asynchronous protocol\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e        5.4. Stale synchronous protocol\u003c\/p\u003e  \u003cp\u003e        5.5. Benchmark\u003c\/p\u003e          5.6. Summary\u003cp\u003e\u003c\/p\u003e  \u003cp\u003eChapter 6: Conclusion\u003c\/p\u003e  \u003cp\u003e        6.1. Summary of the book\u003c\/p\u003e  \u003cp\u003e        6.2. Future work\u003c\/p\u003e","brand":"Springer Verlag, Singapore","offers":[{"title":"Default Title","offer_id":53518301528407,"sku":"9789811634192","price":113.99,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/distributed-machine-learning-and-gradient-optimization-9789811634192","provider":"Book Curl","version":"1.0","type":"link"}