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.;Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. You will: Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production.