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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

Sequence-to-vector models

In the previous section, you technically saw a sequence-to-vector model, which took a sequence (of numbers representing words) and mapped to a vector (of one dimension corresponding to a movie review). However, to appreciate these models further, we will move back to MNIST as the source of input to build a model that will take one MNIST numeral and map it to a latent vector.

Unsupervised model

Let's work in the autoencoder architecture shown in the following diagram. We have studied autoencoders before and now we will use them again since we learned that they are powerful in finding vectorial representations (latent spaces) that are robust and driven by unsupervised learning:

Figure 13.10. LSTM-based autoencoder architecture for MNIST

The goal here is to take an image and find its latent representation, which, in the example of Figure 13.10, would be two dimensions. However, you might be wondering: how can an image be a sequence?

We can interpret an image...

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