Deep Learning Swift Tensorflow Differentiable - Programming Ebook

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Tuesday, February 9, 2021

Deep Learning Swift Tensorflow Differentiable

Deep Learning Swift Tensorflow Differentiable
Deep Learning Swift Tensorflow Differentiable




Book Details 
             TitleDeep Learning Swift Tensorflow Differentiable
         Author: Rahul Bhalley
    Language: English
        SubjectSwift / Computers & Technology / Programming / Apple Programming
No. of pages: 295
         Format: PDF, EPUB

Preface

As a programmer and student pursuing graduation, I had a lot of trouble understanding deep learning by myself when I started this journey back in 2015. So I decided to write a deep learning programming book that might help people in a similar situation as mine to easily understand deep learning. I have tried to keep the explanation of difficult deep learning concepts simple throughout the book.

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But why Swift for deep learning? Swift is a powerful general-purpose differentiable programming language. Swift is a well-researched programming language and the code written in it seems like reading English sentences, making it easy for newcomers to learn programming and even deep learning. Furthermore, Swift is optimized for performance, so researchers can write all deep learning algorithms in a single language with simple syntax.

And what about Python’s wide ecosystem of various libraries? With the Python interoperability feature of Swift for TensorFlow (S4TF), you can use Python libraries within Swift!

The intended audience of this book is as follows:

  1. Newcomers to programming and/or deep learning, expert programmers, and deep learning researchers

  2. People who desire to work in user space instead of framework space with the same programming language in deep learning without compromising the speed

For newcomers, I hope this book serves as a good starting point to learn programming and deep learning. And for researchers, I hope this book will make the adoption of Swift for TensorFlow for deep learning research easy. 


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