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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Getting Started with Deep Learning Using PyTorch

Getting Started with Deep Learning Using PyTorch

Artificial intelligence

Machine learning

Building Blocks of Neural Networks

Building Blocks of Neural Networks

Installing PyTorch

Our first neural network

Diving Deep into Neural Networks

Diving Deep into Neural Networks

Deep dive into the building blocks of neural networks

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

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

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

Convolutional network on sequence data

Generative Networks

Generative Networks

Neural style transfer

Generative adversarial networks

Deep convolutional GAN

Language modeling

Modern Network Architectures

Modern Network Architectures

Modern network architectures

Densely connected convolutional networks – DenseNet

Model ensembling

Encoder-decoder architecture

What Next?

Interesting ideas to explore

How to keep yourself updated

Other Books You May Enjoy

Other Books You May Enjoy

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Artificial intelligence

Countless articles discussing AI are published every day. The trend has increased in the last two years. There are several definitions of AI floating around the web, my favorite being the automation of intellectual tasks normally performed by humans.

The history of AI

The term artificial intelligence was first coined by John McCarthy in 1956, when he held the first academic conference on the subject. The journey of the question of whether machines think or not started much earlier than that. In the early days of AI, machines were able to solve problems that were difficult for humans to solve.

For example, the Enigma machine was built at the end of World War II to be used in military communications. Alan Turing built an AI system that helped to crack the Enigma code. Cracking the Enigma code was a very challenging task for a human, and it could take weeks for an analyst to do. The AI machine was able to crack the code in hours.

Computers have a tough time solving problems that are intuitive to us, such as differentiating between dogs and cats, telling whether your friend is angry at you for arriving late at a party (emotions), differentiating between a truck and a car, taking notes during a seminar (speech recognition), or converting notes to another language for your friend who does not understand your language (for example, French to English). Most of these tasks are intuitive to us, but we were unable to program or hard code a computer to do these kinds of tasks. Most of the intelligence in early AI machines was hard coded, such as a computer program playing chess.

In the early years of AI, a lot of researchers believed that AI could be achieved by hard coding rules. This kind of AI is called symbolic AI and was useful in solving well-defined, logical problems, but it was almost incapable of solving complex problems such as image recognition, object detection, object segmentation, language translation, and natural-language-understanding tasks. Newer approaches to AI, such as machine learning and DL, were developed to solve these kinds of problems.

To better understand the relationships among AI, ML, and DL, let's visualize them as concentric circles with AI—the idea that came first (the largest), then machine learning—(which blossomed later), and finally DL—which is driving today’s AI explosion (fitting inside both):

How AI, machine learning, and DL fit together