This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time applications. This book introduces a broad range of topics in deep learning. The authors start with the fundamentals, architectures, tools needed for effective implementation for scientists. They then present technical exposure towards deep learning using Keras, Tensorflow, Pytorch and Python. They proceed with advanced concepts with hands-on sessions for deep learning. Engineers, scientists, researches looking for a practical approach to deep learning will enjoy this book.

• Presents practical basics to advanced concepts in deep learning and how to apply them through various projects;
• Discusses topics such as deep learning in smart grids and renewable energy & sustainable development;
• Explains how to implement advanced techniques in deep learning using Pytorch, Keras, Python programming.

This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time applications. This book introduces a broad range of topics in deep learning. The authors start with the fundamentals, architectures, tools needed for effective implementation for scientists. They then present technical exposure towards deep learning using Keras, Tensorflow, Pytorch and Python. They proceed with advanced concepts with hands-on sessions for deep learning. Engineers, scientists, researches looking for a practical approach to deep learning will enjoy this book.

• Presents practical basics to advanced concepts in deep learning and how to apply them through various projects;
• Discusses topics such as deep learning in smart grids and renewable energy & sustainable development;
• Explains how to implement advanced techniques in deep learning using Pytorch, Keras, Python programming.

This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time applications. This book introduces a broad range of topics in deep learning. The authors start with the fundamentals, architectures, tools needed for effective implementation for scientists. They then present technical exposure towards deep learning using Keras, Tensorflow, Pytorch and Python. They proceed with advanced concepts with hands-on sessions for deep learning. Engineers, scientists, researches looking for a practical approach to deep learning will enjoy this book.

• Presents practical basics to advanced concepts in deep learning and how to apply them through various projects;
• Discusses topics such as deep learning in smart grids and renewable energy & sustainable development;
• Explains how to implement advanced techniques in deep learning using Pytorch, Keras, Python programming.