Book Image

Deep Learning with PyTorch

By : Vishnu Subramanian
Book Image

Deep Learning with PyTorch

By: Vishnu Subramanian

Overview of this book

Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, TensorFlow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease.

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Table of Contents (11 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Getting Started with Deep Learning Using PyTorch
Getting Started with Deep Learning Using PyTorch
Artificial intelligence
Machine learning
Deep learning
Summary
2
Building Blocks of Neural Networks
Building Blocks of Neural Networks
Installing PyTorch
Our first neural network
Summary
3
Diving Deep into Neural Networks
Diving Deep into Neural Networks
Deep dive into the building blocks of neural networks
Summary
4
Fundamentals of Machine Learning
Fundamentals of Machine Learning
Three kinds of machine learning problems
Machine learning glossary
Evaluating machine learning models
Data preprocessing and feature engineering
Overfitting and underfitting
Workflow of a machine learning project
Summary
5
Deep Learning for Computer Vision
Deep Learning for Computer Vision
Introduction to neural networks
Building a CNN model from scratch
Creating and exploring a VGG16 model
Calculating pre-convoluted features
Understanding what a CNN model learns
Visualizing weights of the CNN layer
Summary
6
Deep Learning with Sequence Data and Text
Deep Learning with Sequence Data and Text
Working with text data
Training word embedding by building a sentiment classifier
Using pretrained word embeddings
Recursive neural networks
LSTM
Convolutional network on sequence data
Summary
7
Generative Networks
Generative Networks
Neural style transfer
Generative adversarial networks
Deep convolutional GAN
Language modeling
Summary
8
Modern Network Architectures
Modern Network Architectures
Modern network architectures
Densely connected convolutional networks – DenseNet
Model ensembling
Encoder-decoder architecture
Summary
9
What Next?
What Next?
What next?
Overview
Interesting ideas to explore
How to keep yourself updated
Summary
10
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Generative adversarial networks

GANs have become very popular in the last few years. Every week there are some advancements being made in the area of GANs. It has become one of the important subfields of deep learning, with a very active research community. GAN was introduced by Ian Goodfellow in 2014. The GAN addresses the problem of unsupervised learning by training two deep neural networks, called generator and discriminator, which compete with each other. In the course of training, both eventually become better at the tasks that they perform.

GANs are intuitively understood using the case of counterfeiter (generator) and the police (discriminator). Initially, the counterfeiter shows the police fake money. The police identifies it as fake and explains to the counterfeiter why it is fake. The counterfeiter makes new fake money based on the feedback it received. The police finds...

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