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Deep Learning Quick Reference

You're reading from   Deep Learning Quick Reference Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788837996
Length 272 pages
Edition 1st Edition
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Author (1):
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Mike Bernico Mike Bernico
Author Profile Icon Mike Bernico
Mike Bernico
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Table of Contents (15) Chapters Close

Preface 1. The Building Blocks of Deep Learning FREE CHAPTER 2. Using Deep Learning to Solve Regression Problems 3. Monitoring Network Training Using TensorBoard 4. Using Deep Learning to Solve Binary Classification Problems 5. Using Keras to Solve Multiclass Classification Problems 6. Hyperparameter Optimization 7. Training a CNN from Scratch 8. Transfer Learning with Pretrained CNNs 9. Training an RNN from scratch 10. Training LSTMs with Word Embeddings from Scratch 11. Training Seq2Seq Models 12. Using Deep Reinforcement Learning 13. Generative Adversarial Networks 14. Other Books You May Enjoy

A brief overview of TensorBoard

TensorBoard is a web-based application that can help you visualize the metrics, parameters, and structure of a deep neural network created in TensorFlow. It will help you debug and optimize your deep neural networks faster and easier.

As you've probably guessed by now, deep neural networks can get quite complex. That, unfortunately, means that there are quite a few things that can go wrong. I've been known to make a mistake every now and then, and when bugs happen inside a deep neural network, which is inside a framework, that runs on another framework, that runs on a GPU, it can be very hard to find these them. TensorBoard can be the flashlight you need to find the problem in an otherwise very dark room. TensorBoard will allow you to monitor the changes in metrics and parameters as your network is trained, which can greatly accelerate...

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