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Deep Learning for Beginners

Deep Learning for Beginners

By : Pablo Rivas, Rivas
4.3 (3)
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Deep Learning for Beginners

Deep Learning for Beginners

4.3 (3)
By: Pablo Rivas, Rivas

Overview of this book

With information on the web exponentially increasing, it has become more difficult than ever to navigate through everything to find reliable content that will help you get started with deep learning. This book is designed to help you if you're a beginner looking to work on deep learning and build deep learning models from scratch, and you already have the basic mathematical and programming knowledge required to get started. The book begins with a basic overview of machine learning, guiding you through setting up popular Python frameworks. You will also understand how to prepare data by cleaning and preprocessing it for deep learning, and gradually go on to explore neural networks. A dedicated section will give you insights into the working of neural networks by helping you get hands-on with training single and multiple layers of neurons. Later, you will cover popular neural network architectures such as CNNs, RNNs, AEs, VAEs, and GANs with the help of simple examples, and learn how to build models from scratch. At the end of each chapter, you will find a question and answer section to help you test what you've learned through the course of the book. By the end of this book, you'll be well-versed with deep learning concepts and have the knowledge you need to use specific algorithms with various tools for different tasks.
Table of Contents (20 chapters)
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1
Section 1: Getting Up to Speed
8
Section 2: Unsupervised Deep Learning
13
Section 3: Supervised Deep Learning

Summary

This chapter showed that autoencoders are very simple models that can be used to encode and decode data for different purposes, such as data compression, data visualization, and simply finding latent spaces where only important features are preserved. We showed that the number of neurons and the number of layers in the autoencoder are important for the success of the model. Deeper (more layers) and wider (more neurons) traits are often ingredients for good models, even if that leads to slower training times.

At this point, you should know the difference between supervised and unsupervised learning in terms of passive learning. You should also feel comfortable implementing the two basic components of an autoencoder: the encoder and the decoder. Similarly, you should be able to modify the architecture of an autoencoder to fine-tune it to achieve better performance. Taking the example we discussed in this chapter, you should be able to apply an autoencoder to a dimensionality reduction...

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