Keras 3 The Comprehensive Guide to Deep Learning with the Keras API and Python
ca. € 59,95
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€ 59,95
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Rheinwerk Computing, ISBN 978-1-4932-2739-6
E-Book-Formate: PDF, EPUB, Online
Rheinwerk Computing, ISBN 978-1-4932-2740-2
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
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Source Code
Complete source code listings. These can also be accessed on the author's webpage: https://recluze.net/keras-book
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Full Color Images
Full color versions of all images used in the book. These can also be accessed on the author's webpage: https://recluze.net/keras-book
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Teaching Slides
Teaching slides prepared for instructors who are using Keras 3: The Comprehensive Guide to Deep Learning with the Keras API and Python by Mohammad Nauman in their courses. The slides are designed to complement the chapters of the book and can be used directly in lectures to explain key concepts, equations, and examples. The slides can be downloaded at: https://github.com/recluze/keras-book-slides
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:
- Neural networks
- Gradient descent
- Classification
- Regularization
- Convolutional neural networks (CNNs)
- Functional API
- Transformer architecture
- Reinforcement learning
- Autoencoders
- Stable Diffusion
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