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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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Generating MNIST images using a Keras GAN

We've worked with MNIST before, but this time we will be generating new MNIST like images with a GAN. It can take a very long time to train a GAN; however, this problem is small enough that it can be run on most laptops in a few hours, which makes it a great example. Later we will expand this example to CIFAR-10 images.

The network architecture that I'm using here has been found by, and optimized by, many folks, including the authors of the DCGAN paper and people like Erik Linder-Norén, who's excellent collection of GAN implementations called Keras GAN (https://github.com/eriklindernoren/Keras-GAN) served as the basis of the code I used here. If you're wondering how I came to the architecture choices I used here, these are the giants whose shoulders I'm attempting to stand upon.

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