Get to grips with the basics of Keras to implement fast and efficient deep-learning models

About This Book

Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games

See how various deep-learning models and practical use-cases can be implemented using Keras

A practical, hands-on guide with real-world examples to give you a strong foundation in Keras

Who This Book Is For

If you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deep-learning with Keras. A knowledge of Python is required for this book.

What You Will Learn

Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm

Fine-tune a neural network to improve the quality of results

Use deep learning for image and audio processing

Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases

Identify problems for which Recurrent Neural Network (RNN) solutions are suitable

Explore the process required to implement Autoencoders

Evolve a deep neural network using reinforcement learning

In Detail

This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer.

Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks.

Style and approach

This book is an easy-to-follow guide full of examples and real-world applications to help you gain an in-depth understanding of Keras. This book will showcase more than twenty working Deep Neural Networks coded in Python using Keras.