Progressive Deep Learning with Keras in Practice

Keras is an (Open source Neural Network library written in Python) Deep Learning library for fast, efficient training of Deep Learning models. It is a minimal, highly modular framework that runs on both CPUs and GPUs, and allows you to put your ideas into action in the shortest possible time. Because it is lightweight and very easy to use, Keras has gained quite a lot of popularity in a very short time.

This comprehensive 3–in–1 course takes a step–by–step practical approach to implement fast and efficient Deep Learning models: Projects on Image Processing, NLP, and Reinforcement Learning. Initially, you’ll learn backpropagation, install and configure Keras and understand callbacks and for customizing the process. You’ll build, train, and run fully–connected, Convolutional and Recurrent Neural Networks. You’ll also solve Supervised and Unsupervised learning problems using images, text and time series. Moving further, you’ll use concepts, intuitive understating and applications of Autoencoders and Generative Adversarial Networks. Finally, you’ll build projects on Image Processing, NLP, and Reinforcement Learning and build cutting–edge Deep Learning models in a simple, easy to understand way.

Towards the end of this course, you’ll get to grips with the basics of Keras to implement fast and efficient Deep Learning models: Projects on Image Processing, NLP, and Reinforcement Learning.