Nauman M. Keras 3. Hands-On Deep Learning with Python, Neural Networks,...2025

Textbook in PDF format

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
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.
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.
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.
Neural networks
Gradient descent
Classification
Regularization
Convolutional neural networks (CNNs)
Functional API
Transformer architecture
Reinforcement learning
Autoencoders
Stable Diffusion