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AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence-Laurence Moroney

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Ebook About
If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics.You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code.You'll learn:How to build models with TensorFlow using skills that employers desireThe basics of machine learning by working with code samplesHow to implement computer vision, including feature detection in imagesHow to use NLP to tokenize and sequence words and sentencesMethods for embedding models in Android and iOSHow to serve models over the web and in the cloud with TensorFlow Serving

Book AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence Review :



Tonight, I completed reading of Laurence Moroney’s book “AI and Machine Learning for Coders”. I enjoyed this book from cover to cover, and I am especially thankful for a few things.First, this book did a great job of explaining how tensorflow can be used to solve different types of problems, such as computer vision, natural language processing, and time series forecasting. It even went into text generation, which got my creativity going.Second, this book is written for coders, but it does explain how neural networks work at a high level. It hust doesn’t drop a wall of math on the reader, which I appreciated. I feel much more comfortable now, after reading this.And finally, the book ends by discussing interpretability, bias/fairness, and Google’s AI principles. I found that to be a beautiful ending.I give this five stars. The few issues I had were trivial. This book is outstanding. I’m so much better off for having read it.
If you start with this book, watch Laurence's videos on the TensorFlow/ Google Developer youtube channel and work all the examples and exercises, you will have a solid foundation in deep learning. Then you could potentially learn more from the documentation itself or by using Aurélien Géron's book.I feel other Oreilly books should be like this.Although math is almost absent in this book (hence 'for coders'), Laurence does a great job explaining convolutions and maxpooling etc.. I've seen lectures from many sources that misattribute pixel/dimension reduction to the convolution step and not the pooling layer (worse yet, lump both together) but the author clearly explains the concepts.

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