The Best Gradient Descent Books of All Time

This book introduces the fundamendals of Artificial Intelligence (AI). It is written by an High Schooler for High Schoolers. It explains in depth the mathematics behind the Gradient Descent method, which is crucial to how AI functions. This book delves into multiple facets of AI, and due to the simplistic language, allows for a reader understanding unlike most publications on this subject.

This 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. Written 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 appealto a broad audience in the field of machine learning, artificial intelligence, big data and database management.

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