Deep Learning Quick Reference

Dive deeper into neural networks and get your models trained, optimized with this quick reference guide.

Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples.

You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks.

What You Will Learn:
Solve regression and classification challenges with TensorFlow and Keras
Learn to use Tensor Board for monitoring neural networks and its training
Optimize hyperparameters and safe choices/best practices
Build CNN's, RNN's, and LSTM's and using word embedding from scratch
Build and train seq2seq models for machine translation and chat applications.
Understanding Deep Q networks and how to use one to solve an autonomous agent problem.
Explore Deep Q Network and address autonomous agent challenges.