Book Image

Deep Learning Quick Reference

By : Mike Bernico
Book Image

Deep Learning Quick Reference

By: Mike Bernico

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)

The impact of source/target volume and similarity

Until somewhat recently, there has been very little investigation into the impact that data volume and source/target domain similarity have played in transfer learning performance; however, it's a topic important to the usability of transfer learning and a topic I've written about. In the paper Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications, (https://arxiv.org/pdf/1712.04008.pdf), written by my colleagues, Yuntao Li, Dingchao Zhang, and myself, we did some experimentation on these topics. Here's what we found.

More data is always beneficial

In several experiments conducted by Google researchers in the paper Revisiting...