Keras 3: Hands-On Deep Learning with Python, Neural Networks, CNNs, and Generative AI Models (Rheinwerk Computing)

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
Review the foundations of deep learning and neural networks. Understand how core concepts like gradient descent, classification, and regularization help fine-tune your models and minimize loss function.

Model Development and Training
Follow step-by-step instructions to build models in Keras: develop convolutional neural networks (CNNs), apply the functional API for multi-layer 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.

Dr. Mohammad Nauman is a seasoned machine learning expert with more than 20 years of teaching experience and a track record of educating 40,000+ students globally through his paid and free online courses on platforms like Udemy and YouTube. He holds a PhD in computer science and was a post-doctoral fellow at the Max Planck Institute for Software Systems in Germany. His groundbreaking post-doc research focused on applying machine learning to advance security and privacy solutions. Dr. Nauman’s teaching philosophy—rooted in bridging theory and practice—empowers learners to master tools while building robust foundational skills, whether in academic settings or through his widely accessible digital programs.