Keras Deep Learning Cookbook - Tapa blanda

Reseña del editor

Leverage the power of deep learning and Keras to develop smarter and more efficient data models

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

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

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.

Table of Contents

1. Installing and Setting up Keras
2. Working with datasets and models
3. Data Preprocessing, Optimization and Visualization
4. Classifying text using different Keras layers
5. Implementing Convolutional Neural Networks
6. Generative Adversarial Networks
7. Implementing Recurrent Neural Networks
8. Natural Language Processing using Keras Models
9. Text summarization using Keras Models
10. Reinforcement learning using Keras Models

Biografía del autor

Rajdeep Dua has over 18 years experience in the cloud and big data space. He has taught Spark and big data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and Pune College of Engineering. He currently leads the developer relations team at Salesforce India. He has also presented BigQuery and Google App Engine at the W3C conference in Hyderabad. He led the developer relations teams at Google, VMware, and Microsoft, and has spoken at hundreds of other conferences on the cloud. Some of the other references to his work can be seen at Your Story and on ACM digital library. His contributions to the open source community relate to Docker, Kubernetes, Android, OpenStack, and Cloud Foundry. Manpreet Singh Ghotra has more than 15 years experience in software development for both enterprise and big data software. He is currently working at Salesforce on developing a machine learning platform/APIs using open source libraries and frameworks such as Keras, Apache Spark, and TensorFlow. He has worked on various machine learning systems, including sentiment analysis, spam detection, and anomaly detection. He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout, and the R recommendation system, again using Apache Mahout. With a master's and postgraduate degree in machine learning, he has contributed to, and worked for, the machine learning community.