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