Modern Keras The Comprehensive Guide to Deep Learning with the Keras API and Python
written by
Mohammad Nauman
500 pages, 2026, Print edition paperback
ISBN 978-1-4932-2739-6 500 pages, 2026
E-book formats: EPUB, PDF, online
ISBN 978-1-4932-2740-2 500 pages, 2026, Print edition paperback
E-book formats: EPUB, PDF, online
ISBN 978-1-4932-2741-9
ISBN 978-1-4932-2739-6 500 pages, 2026
E-book formats: EPUB, PDF, online
ISBN 978-1-4932-2740-2 500 pages, 2026, Print edition paperback
E-book formats: EPUB, PDF, online
ISBN 978-1-4932-2741-9
Harness the power of AI with this guide to using Keras! Start by reviewing the fundamentals of deep learning and installing the Keras API. Next, follow Python code examples to build your own models, and then train them using classification, gradient descent, and regularization. Design large-scale, multilayer models and improve their decision making with reinforcement learning. With tips for creating generative AI models, this is your cutting-edge resource for working with deep learning!
- Learn to use Keras for deep learning
- Work with techniques such as gradient descent, classification, regularization, and more
- Build and train convolutional neural networks, transformers, and autoencoders
About the Book
About the E-book
500 pages, paperback. Including code download. Reference book format 7 x 10 in. Printed black and white on 60# offset paper from sustainable sources. Reader-friendly serif font (TheAntiquaB 9.5 Pt.). One-column layout.
E-book in full color. PDF and EPUB files for download, DRM-free with personalized digital watermark. Copy and paste, bookmarks, and print-out permitted. Table of contents, in-text references, and index fully linked. Including online book edition in dedicated reader application.
In this book, you'll learn about:
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Deep Learning Basics
Understand the foundations of deep learning, machine learning, and neural networks. Learn core concepts like gradient descent, classification, and regularization to fine-tune your models and minimize loss function.
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Model Development and Training
Follow step-by-step instructions to build models in Keras: develop a convolutional neural network, apply the functional API for complex models, and implement transformer architecture. Use reinforcement learning to improve your models’ decision-making.
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Generative AI Models
Build and train your own generative AI models! Get hands-on with text to image techniques and work with variational autoencoders and generative adversarial networks.
Highlights include:
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Neural networks
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Gradient descent
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Classification
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Regularization
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Convolutional neural networks (CNNs)
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Functional API
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Transformer architecture
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Reinforcement learning
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Autoencoders
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Stable Diffusion