- You should have prior knowledge of Keras and be comfortable with Python and have basic understanding of Machine Learning. Additionally, refer to the code files in GitHub while you are going through recipes. Due care is taken to ensure that all code is properly indented in book. However, it is highly recommended that you follow the code present in GitHub while implementing the code yourself
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Neural Networks with Keras Cookbook
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Neural Networks with Keras Cookbook
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Overview of this book
This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach.
We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data.
Later, we will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks.
We will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems.
Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game.
By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter.
Table of Contents (18 chapters)
Preface
Free Chapter
Building a Feedforward Neural Network
Building a Deep Feedforward Neural Network
Applications of Deep Feedforward Neural Networks
Building a Deep Convolutional Neural Network
Transfer Learning
Detecting and Localizing Objects in Images
Image Analysis Applications in Self-Driving Cars
Image Generation
Encoding Inputs
Text Analysis Using Word Vectors
Building a Recurrent Neural Network
Applications of a Many-to-One Architecture RNN
Sequence-to-Sequence Learning
End-to-End Learning
Audio Analysis
Reinforcement Learning
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