Neural Networks with Keras Cookbook

This book equips readers with the skills to build neural networks from scratch, covering various applications and architectures in deep learning.

The book Neural Networks with Keras Cookbook provides a comprehensive guide to overcoming the challenges of training neural networks for deep learning applications. It covers a range of trending architectures currently used in the industry, addressing various use cases in fields such as computer vision, text processing, audio analysis, recommender systems, and game bots. This resource is designed to empower readers to build neural network architectures from scratch for real-world applications.

In Neural Networks with Keras Cookbook, readers will start with the foundational concepts of neural networks and progress to advanced implementations using a recipe-based approach. The book delves into the workings of neural networks and the influence of hyperparameters on accuracy, while also exploring how to leverage these networks for structured and unstructured data. Key topics include object classification and detection in images, transfer learning applications, and the use of Convolutional Neural Networks for tasks such as self-driving cars.

The book also covers the generation of images through Generative Adversarial Networks (GANs), text analysis using word vector techniques, and the implementation of chatbots and machine translation systems using Recurrent Neural Networks and LSTM. By the end of Neural Networks with Keras Cookbook, readers will have the skills necessary to select and customize various neural network architectures to tackle a wide range of deep learning problems.