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Deep Learning Quick Reference

Deep Learning Quick Reference

By : Mike Bernico
4.5 (6)
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Deep Learning Quick Reference

Deep Learning Quick Reference

4.5 (6)
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)
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Summary

In this chapter, we looked at GANs and how they can be used to generate new images. We learned a few rules for building GANs well, and we even learned to simulate MNIST and CIFAR-10 images. There is no doubt that you've probably seen some amazing images, created by GANs, in the media. After reading this chapter and working through these examples, you have the tools to do the same. I hope that you can take these ideas and adapt them. The only limitations left are your own imagination, your data, and your GPU budget.

In this book we covered a great many applications of deep learning, from simple regression to Generative Adversarial Networks. My greatest hope for this book is that it might help you make practical use of deep learning techniques, many of which have existed in the domain of academia and research, outside the reach of the practicing data scientist or machine...

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