Deep Learning with Swift for TensorFlow: Differentiable Programming with Swift - Softcover

Über die Autorin bzw. den Autor

Rahul Bhalley is an independent machine intelligence researcher. He was the co-founder of a short-lived deep learning startup in 2018. He has published research papers in areas such as speech processing and generative modeling. He actively contributes to open source projects related to deep learning on GitHub. He has also worked with Apple's Swift and shares Google's vision of making it easy for others to understand deep learning with Swift.

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About this book

Discover more insight about deep learning algorithms with Swift for TensorFlow. The Swift language was designed by Apple for optimized performance and development whereas TensorFlow library was designed by Google for advanced machine learning research. Swift for TensorFlow is a combination of both with support for modern hardware accelerators and more. This book covers the deep learning concepts from fundamentals to advanced research. It also introduces the Swift language for beginners in programming. This book is well suited for newcomers and experts in programming and deep learning alike. After reading this book you should be able to program various state-of-the-art deep learning algorithms yourself.

 

The book covers foundational concepts of machine learning. It also introduces the mathematics required to understand deep learning. Swift language is introduced such that it allows beginners and researchers to understand programming and easily transit to Swift for TensorFlow, respectively. You will understand the nuts and bolts of building and training neural networks, and build advanced algorithms.

 

What You ll Learn

 Understand deep learning concepts

 Program various deep learning algorithms

 Run the algorithms in cloud

 

Who This Book Is For

 Newcomers to programming and/or deep learning, and experienced developers.

 Experienced deep learning practitioners and researchers who desire to work in user space instead of library space with a same programming language without compromising the speed