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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 intermediate-introductory chapter showed the design of an MLP and the paradigms surrounding its functionality. We covered the theoretical framework behind its elements and we had a full discussion and treatment of the widely known backprop mechanism to perform gradient descent on a loss function. Understanding the backprop algorithm is key for further chapters since some models are designed specifically to overcome some potential difficulties with backprop. You should feel confident that what you have learned about backprop will serve you well in knowing what deep learning is all about. This backprop algorithm, among other things, is what makes deep learning an exciting area. Now, you should be able to understand and design your own MLP with different layers and different neurons. Furthermore, you should feel confident in changing some of its parameters, although we will cover more of this in the further reading.

Chapter 7, Autoencoders, will continue with an architecture...

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