Machine Learning with TensorFlow (MEAP v.10)-Manning(2018).pdf下载

Dear fellow early adopters, curious readers, and puzzled newcomers, Thank you all for every bit of communication with me, whether it be through the official book forums, through email, on GitHub, or even on Reddit. I’ve listened carefully to your questions, suggestions, and concerns, regardless of whether or not I’ve replied to you (and I do apologize for not replying to you). In the latest edition, I am proud to announce a beautiful makeover of every chapter. The text is greatly improved and slowed down to better cover complex matters, especially the areas where you requested more explanation. Most figures and mathematical equations have been updated to look crisp and professional. The code is now updated to TensorFlow v1.0, and it is also available on GitHub at https://github.com/BinRoot/TensorFlow-Book/. Also, the chapters are rearranged to better deliver the right skills at the right time, if the book were read in order. Thank you for investing in the MEAP edition of Machine Learning with TensorFlow. You’re one of the first to dive into this introductory book about cutting-edge machine learning techniques using the hottest technology (spoiler alert: I’m talking about TensorFlow). You’re a brave one, dear reader. And for that, I reward you generously with the following. You’re about to learn machine learning from scratch, both the theory and how to easily implement it. As long as you roughly understand object-oriented programming and know how to use Python, this book will teach you everything you need to know to start solving your own big-data problems, whether it be for work or research. TensorFlow was released just over a year ago by some company that specializes in search engine technology. Okay, I’m being a little facetious; well-known researchers at Google engineered this library. But with such prowess comes intimidating documentation and assumed knowledge. Fortunately for you, this book is down-to-earth and greets you with open arms. Each chapter zooms into a prominent example of machine learning, such as classification, regression, anomaly detection, clustering, and many modern neural networks. Cover them all to master the basics, or cater it to your needs by skipping around. Keep me updated on typos, mistakes, and improvements because this book is undergoing heavy development. It’s like living in a house that’s still actively under construction; at least you won’t have to pay rent. But on a serious note, your feedback along the way will be appreciated.