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.