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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

Case study – epileptic seizure recognition

As you've probably guessed, we are going to be solving a binary classification problem. We will start by planning the problem using the same framework we established in Chapter 2, Using Deep Learning to Solve Regression Problems, and modify it as needed. You can find the complete code for this chapter in the book's GitHub repository, under Chapter 4, Using Deep Learning to Solve Regression Problems.

Defining our dataset

The dataset that we will be working on this chapter is called the Epileptic Seizure Recognition dataset. The data originally comes from a paper titled Indications of nonlinear deterministic and finite dimensional structures in time series of brain...

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