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
Other Books You May Enjoy
Other Books You May Enjoy
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Language modeling

We will learn how to teach a recurrent neural network (RNN) how it can create a sequence of text. In simple words, the RNN model that we will build now will be able to predict the next word, given some context. This is just like the Swift app on your phone, which guesses the next word that you are typing. The ability to generate sequential data has applications in many different areas, such as:

  • Image captioning
  • Speech recognition
  • Language translation
  • Automatic email reply

We learnt in Chapter 6, Deep Learning with Sequence Data and Text, that RNNs are tough to train. So, we will be using a variant of RNN called Long Short-Term Memory (LSTM). The development of the LSTM algorithm started in 1997 but became popular in the last few years. It became popular due to the availability of powerful hardware and quality data, and some advancements such as dropout also...

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