Effective Machine Learning

Learn to use machine learning tools in practice efficiently.

Table of Contents

1. PyTorch basics 2. Encapsulate your model with Modules 3. Broadcasting the good and the ugly 4. Take advantage of the overloaded operators 5. Optimizing runtime with TorchScript 6. Building efficient custom data loaders 7. Numerical stability in PyTorch 8. Faster training with mixed precision

Table of Contents

01. TensorFlow basics 02. Understanding static and dynamic shapes 03. Scopes and when to use them 04. Broadcasting the good and the ugly 05. Feeding data to TensorFlow 06. Take advantage of the overloaded operators 07. Understanding order of execution and control dependencies 08. Control flow operations: conditionals and loops 09. Prototyping kernels and advanced visualization with Python ops 10. Multi-GPU processing with data parallelism 11. Debugging TensorFlow models 12. Numerical stability in TensorFlow 13. Building a neural network training framework with learn API 14. TensorFlow Cookbook

Table of Contents

01. TensorFlow basics 02. Broadcasting the good and the ugly 03. Take advantage of the overloaded operators 04. Control flow operations: conditionals and loops 05. Prototyping kernels and advanced visualization with Python ops 06. Numerical stability in TensorFlow

Table of Contents

01. TensorFlow.js basics 02. Using pretrained models 03. Tiling and broadcasting