Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

This definitive guide delves into the breadth of deep learning methodologies, emphasizing hands-on applications with TensorFlow 2 and Keras. Readers are introduced to advanced topics such as generative models, object detection, unsupervised learning, and deep reinforcement learning, equipping them with the tools to develop cutting-edge AI solutions.

What this Book will help me do

• Design and implement advanced neural networks such as ResNet and DenseNet for real-world tasks.
• Execute generative model approaches including GANs and VAEs for data generation and feature learning.
• Apply deep reinforcement learning algorithms like Deep Q-Learning and Policy Gradient Methods.
• Develop object detection systems and semantic segmentation models for accurate image processing.
• Master unsupervised techniques using mutual information for diverse data applications.

Author(s)

Rowel Atienza is an accomplished data scientist and researcher specializing in deep learning and artificial intelligence. With a rich background in both industry and academia, he brings practical insights along with theoretical depth. His writing style combines clarity and rigor, providing a conversational yet comprehensive approach to advanced technical topics.

Who is it for?

This book is crafted for data scientists, AI researchers, and machine learning engineers seeking to master state-of-the-art deep learning techniques. Ideal readers possess foundational Python skills and basic knowledge of machine learning concepts. While familiarity with TensorFlow or Keras is recommended, the book's content is accessible to those looking to deepen their expertise in advanced neural networks and AI solutions.