Items related to Deep Learning with Python: A Hands-on Introduction
Synopsis
Chapter 1: An intuitive look at the fundamentals of deep learning based on practical applications
Chapter 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 ecosystem
Chapter 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 learning
Chapter 4: A detailed look at Theano [2], which is a low level framework for implementing architectures and algorithms in deep learning from scratch
Chapter 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 Learning
Chapter 7: A brief introduction to Automatic Differentiation
Chapter 8: A brief introduction to Backpropagation and Stochastic Gradient Descent
Chapter 9: A survey of Deep Learning Architectures
Chapter 10: Advice on running large scale experiments in deep learning and taking models to production
Chapter 11: Introduction to Tensorflow
Chapter 12: Introduction to PyTorch
Chapter 13: Regularization Techniques
Chapter 14: Training Deep Leaning Models
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