▶Book Description
Keras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy.
The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks.
By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning
▶What You Will Learn
⦁ Install and configure Keras in TensorFlow
⦁ Master neural network programming using the Keras library
⦁ Understand the different Keras layers
⦁ Use Keras to implement simple feed-forward neural networks, CNNs and RNNs
⦁ Work with various datasets and models used for image and text classification
⦁ Develop text summarization and reinforcement learning models using Keras
▶Key Features
⦁ Understand different neural networks and their implementation using Keras
⦁ Explore recipes for training and fine-tuning your neural network models
⦁ Put your deep learning knowledge to practice with real-world use-cases, tips, and tricks
▶Who This Book Is For
Keras Deep Learning Cookbook is for you if you are a data scientist or machine learning expert who wants to find practical solutions to common problems encountered while training deep learning models. A basic understanding of Python and some experience in machine learning and neural networks is required for this book.
▶What this book covers
⦁ Chapter 1, Keras Installation, covers various installation and setup procedures, as well as defining various Keras configurations.
⦁ Chapter 2, Working with Keras Datasets and Models, covers using various datasets, such as CIFAR10, CIFAR100, or MNIST, and many other datasets and models used for image classification.
⦁ Chapter 3, Data Preprocessing, Optimization, and Visualization, covers various preprocessing and optimization techniques using Keras. The optimization techniques covered include TFOptimizer, AdaDelta, and many more.
⦁ Chapter 4, Classification Using Different Keras Layers, details various Keras layers, for example, recurrent layers, and convolutional layers.
⦁ Chapter 5, Implementing Convolutional Neural Networks, teaches you convolutional neural network algorithms in detail, using the example of cervical cancer classification and the digit recognition dataset.
⦁ Chapter 6, Generative Adversarial Networks, covers basic generative adversarial networks (GANs) and boundary-seeking GAN.
⦁ Chapter 7, Recurrent Neural Networks, covers the basics of recurrent neural networks in order to implement Keras based on historical datasets.
⦁ Chapter 8, Natural Language Processing Using Keras Models, covers the basics of NLP for word analysis and sentiment analysis using Keras.
⦁ Chapter 9, Text Summarization Using Keras Models, shows you how to use Keras models for text summarization when using the Amazon reviews dataset.
⦁ Chapter 10, Reinforcement Learning, focuses on formulating and developing reinforcement learning models using Keras.
