*Deep Learning Quick Reference* demonstrates a fast and practical approach to using deep learning. It's focused on real-life problems, and it provides just enough theory and math to reinforce the readers' understanding of the topic. Deep learning is an exciting, fast paced branch of machine learning, but it's also a field that can be broken into. It's a field where a flood of detailed, complicated research is created every day, and this can be overwhelming. In this book, I focus on teaching you the skills to apply deep learning on a variety of practical problems. My greatest hope for this book is that it will provide you with the tools you need to use deep learning techniques to solve your machine learning problems.

#### Deep Learning Quick Reference

##### By :

#### Deep Learning Quick Reference

##### By:

#### Overview of this book

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.
By the end of this book, you will be able to solve real-world problems quickly with deep neural networks.

Table of Contents (15 chapters)

Preface

Free Chapter

The Building Blocks of Deep Learning

Using Deep Learning to Solve Regression Problems

Monitoring Network Training Using TensorBoard

Using Deep Learning to Solve Binary Classification Problems

Using Keras to Solve Multiclass Classification Problems

Hyperparameter Optimization

Training a CNN from Scratch

Transfer Learning with Pretrained CNNs

Training an RNN from scratch

Training LSTMs with Word Embeddings from Scratch

Training Seq2Seq Models

Using Deep Reinforcement Learning

Generative Adversarial Networks

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