Deep Learning with Python: A Hands-On Introduction

Chapter 1: An intuitive look at the fundamentals of deep learning based on practical applicationsChapter 2: A survey of the current state-of-the-art implementations of libraries, tools and packages for deep learning and the case for the Python ecosystemChapter 3: A detailed look at Keras [1], which is a high level framework for deep learning suitable for beginners to understand and experiment with deep learningChapter 4: A detailed look at Theano [2], which is a low level framework for implementing architectures and algorithms in deep learning from scratchChapter 5: A detailed look at Caffe [3], which is highly optimized framework for implementing some of the most popular deep learning architectures (mainly computer vision)Chapter 6: A brief introduction to GPUs and why they are a game changer for Deep LearningChapter 7: A brief introduction to Automatic DifferentiationChapter 8: A brief introduction to Backpropagation and Stochastic Gradient DescentChapter 9: A survey of Deep Learning ArchitecturesChapter 10: Advice on running large scale experiments in deep learning and taking models to productionChapter 11: Introduction to TensorflowChapter 12: Introduction to PyTorchChapter 13: Regularization TechniquesChapter 14: Training Deep Leaning Models