About the Book¶
Table of Contents (TOC)¶
Chapter 1: Optimisation and Neural Networks
Chapter 2: Applications with one single neuron
Chapter 3: Feed Forward Neural Networks
Chapter 4: Regularisation
Chapter 5: Optimizers
Chapter 6: Hyperparameter Tuning
Chapter 7: Convolutional Neural Networks
Chapter 8: Recurrent Neural Networks
Chapter 9: Autoencoders
Chapter 10: Metric Analysis
Chapter 11: Generative Adversarial Networks
Appendix 1: Introduction to Keras
Appendix 2: Customising Keras
Short Description (from the back of the cover)¶
Understand how neural networks work and learn how to implement them using TensorFlow 2.0 and Keras. This new edition focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This book is designed so that you can focus on the parts you are interested in. You will explore topics as regularization, optimizers, optimization, metric analysis, and hyper-parameter tuning. In addition, you will learn the fundamentals ideas behind autoencoders and generative adversarial networks. All the code presented in the book will be available in the form of Jupyter notebooks which would allow you to try out all examples and extend them in interesting ways. A companion online book is available with the complete code for all examples discussed in the book and additional material more related to TensorFlow and Keras. All the code will be available in Jupyter notebook format and can be opened directly in Google Colab (no need to install anything locally) or downloaded on your own machine and tested locally. You will: • Understand the fundamental concepts of how neural networks work • Learn the fundamental ideas behind autoencoders and generative adversarial networks • Be able to try all the examples with complete code examples that you can expand for your own projects • Have available a complete online companion book with examples and tutorials.