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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

When transfer learning should be used

Transfer learning works really well when you have limited data and when a network exists that solves a similar problem. You can use transfer learning to bring state-of-the art networks and giant volumes of data to an otherwise small problem. So, when should you use transfer learning? Anytime you can! But, there are two stipulations that I'd like you to think about first. We will discuss them in the following sections.

Limited data

The question I'm most often asked when it comes to computer vision and transfer learning is: How many images do I have to have? It's a difficult question to answer because, as we will see in the next section, more is usually better. A better question...

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