Learn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of fields, from biology to computer vision to business. With nine in-depth case studies, this book will ground you in creative, real-world deep learning thinking.  

You’ll begin with a structured guide to using Keras, with helpful tips and best practices for making the most of the framework. Next, you’ll learn how to train models effectively with transfer learning and self-supervised pre-training. You will then learn how to use a variety of model compressions for practical usage. Lastly, you will learn how to design successful neural network architectures and creatively reframe difficult problems into solvable ones. You’ll learn not only to understand and apply methods successfully but to think critically about it. 

Modern Deep Learning Design and Methods is ideal for readers looking to utilize modern, flexible, and creative deep-learning design and methods. Get ready to design and implement innovative deep-learning solutions to today’s difficult problems. 

What You’ll Learn

• Improve the performance of deep learning models by using pre-trained models, extracting rich features, and automating optimization.
• Compress deep learning models while maintaining performance.
• Reframe a wide variety of difficult problems and design effective deep learning solutions to solve them.
• Use the Keras framework, with some help from libraries like HyperOpt, TensorFlow, and PyTorch, to implement a wide variety of deep learning approaches.

Who This Book Is For

Data scientists with some familiarity with deep learning to deep learning engineers seeking structured inspiration and direction on their next project. Developers interested in harnessing modern deep learning methods to solve a variety of difficult problems.

Table of Contents

Chapter 1: A Deep Dive into Keras

Chapter 2: Pretraining Strategies and Transfer Learning

Chapter 3: The Versatility of Autoencoders

Chapter 4: Model Compression for Practical Deployment

Chapter 5: Automating Model Design with Meta-optimization

Chapter 6: Successful Neural Network Architecture Design

Chapter 7: Reframing Difficult Deep Learning Problems

About the Author

Andre Ye is a data science writer and editor; he has written over 300 data science articles for various top data science publications with over ten million views. He is also a cofounder at Critiq, a peer revision platform that uses machine learning to match users’ essays. In his spare time, Andre enjoys keeping up with current deep learning research, playing the piano, and swimming.