Hacking TensorFlow Internals
Hacking TensorFlow Internals
An Insider’s Commentary on A Learning System
About the Book
This book is an attempt to decipher the internals of TensorFlow in gory details, including (but not limited to) what, how and why from a hacker’s perspective, explaining in detail the nucleus of one of the most interesting learning systems to appear in recent years. This analyses the kernel and reveals the system’s innards including architecture, programming model, tensors, graphs (computational and calculational), gradients, optimizers, clusters and other data structures with algorithms in play. This provides illustrated commentary on code snippets with annotations. The commentary also remarks on how the code might be improved.
These topics will help the programmer to learn, appreciate, modify and extend the TensorFlow Core, which in turn will help improve the TensorFlow system design and performance optimization. C++ Code enthusiasts (both inside and outside the Google Inc.) will be better equipped to hack it to their tastes and needs.
After reading this book, the reader will be on par with the core team of TensorFlow who conceptualized and crafted a new programming model to address problems in machine learning, deep learning, computer vision along with related sub-disciples and will be able to extend it further by sharing the vision.
- First book of its kind on TensorFlow Internals
- It will attract C++ programmers too. Typically this field is donned by Python and R programmers.
- The only exposition of the workings of a 'real' learning system.
- The only TensorFlow kernel documentation available outside Google. (I doubt if one such exists inside Google !)
Typical books on TensorFlow focus on its usage, whereas this book will allow classroom use of the source code.
This book is primarily for the C++ programmer who is keen to unravel the mystical nuances buried deep inside the code of TensorFlow Core. Familiarity with programming in C++ and python with some background in linear algebra, calculus, statistics and machine learning is assumed. Other data science practitioners and instructors may also get benefited by embracing the only commentary available on TensorFlow internals.
In my opinion, it is highly beneficial for practitioners of data science to have the opportunity to study a working learning system in all its aspects.
Moreover it is undoubtedly good for students majoring in Data Science, to be confronted at least once in their careers, with the task of reading and understanding a learning program of major dimensions.
Chapter 1 : Genesis
Chapter 2 : Introduction
Chapter 3 : Programming Model
Chapter 4 : System Architecture
- Single Machine
Chapter 5 : Source Code Structure
Chapter 6 : Data Structures (300+)
Chapter 7 : Algorithms (400+)
Chapter 8 : Source Code Analysis
Chapter 9 : Programming Idioms
Chapter 10 : Design Patterns
Chapter 11 : Optimization
Chapter 12 : Visualization
Chapter 13 : APIs
Chapter 14 : Alternatives
Chapter 15 : Extensions
Chapter 16 : Traps and Pitfalls
Appendix A : Linear Algebra
Appendix B : Matrix Calculus
Appendix C : Probability
Appendix D : Statistics
Appendix E : SWIG
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