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Deep Learning Quick Reference

You're reading from  Deep Learning Quick Reference

Product type Book
Published in Mar 2018
Publisher Packt
ISBN-13 9781788837996
Pages 272 pages
Edition 1st Edition
Languages
Author (1):
Mike Bernico Mike Bernico
Profile icon Mike Bernico

Table of Contents (15) Chapters

Preface 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 Other Books You May Enjoy

Using deep neural networks for regression

Now that you hopefully understand why you would (and would not) want to use deep neural networks for regression, I'll show you how to do it. While it's not quite as simple as using linear regressor in scikit-learn, I think you'll find it quite easy using Keras. Most importantly, Keras will allow you to quickly iterate through model architectures without changing a lot of code.

How to plan a machine learning problem

When building a new neural network, I recommend following the same basic steps every time.

Deep neural networks can get very complicated, very quickly. A little bit of planning and organization and greatly accelerate your workflow!

The following are the steps...

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