Neural Networks with Keras Cookbook: Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots

Neural Networks with Keras Cookbook: Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots
Neural Networks with Keras Cookbook: Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots by V Kishore Ayyadevara
English | 2019 | ISBN: 1789346640 | 568 Pages | EPUB | 40 MB

Implement neural network architectures by building them from scratch for multiple real-world applications.
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.
What you will learn

  • Build multiple advanced neural network architectures from scratch
  • Explore transfer learning to perform object detection and classification
  • Build self-driving car applications using instance and semantic segmentation
  • Understand data encoding for image, text and recommender systems
  • Implement text analysis using sequence-to-sequence learning
  • Leverage a combination of CNN and RNN to perform end-to-end learning
  • Build agents to play games using deep Q-learning