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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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Multiclass classification and deep neural networks

Here it is! We've finally gotten to the fun stuff! In this chapter, we will be creating a deep neural network that can classify an observation into multiple classes, and this is one of those places where neural networks really do well. Let's talk just a bit more about the benefit of deep neural networks for this class of problems.

Just so we're all talking about the same thing, let's define multiclass classification before we begin. Imagine we had a classifier that had, as inputs, the weights of various fruits and would predict the fruit given the weight. The output might be exactly one class in a set of classes (apple, banana, mango, and so on). That's multiclass classification, not to be confused with multilabel, which is the situation where a model might predict whether or not a set of labels will apply...

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