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Building Blocks of Neural Networks

Understanding the basic building blocks of a neural network, such as tensors, tensor operations, and gradient descents, is important for building complex neural networks. In this chapter, we will build our first Hello world program in neural networks by covering the following topics:

Installing PyTorch
Implementing our first neural network
Splitting the neural network into functional blocks
Walking through each fundamental block covering tensors, variables, autograds, gradients, and optimizers
Loading data using PyTorch

Key benefits

Learn PyTorch for implementing cutting-edge deep learning algorithms.

Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios;

Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples;

Description

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.

Who is this book for?

This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected.

What you will learn

• Use PyTorch for GPU-accelerated tensor computations

• Build custom datasets and data loaders for images and test the models using torchvision and torchtext

• Build an image classifier by implementing CNN architectures using PyTorch

• Build systems that do text classification and language modeling using RNN, LSTM, and GRU

• Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning

• Learn how to mix multiple models for a powerful ensemble model

• Generate new images using GAN's and generate artistic images using style transfer

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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P. Romero Jun 11, 2019

If you want in-depth learning on PyTorch, look no further. The author succeeded in presenting practical knowledge on PyTorch that the reader can easily put to use. Recommended.

Amazon Verified review

J K Mar 04, 2019

A very good book for startersHands on training examples are good.The book is must have for deep learning enthusiasts

archit gupta Nov 01, 2018

Nice book

Sumesh Oct 21, 2018

Very good book for beginners.

krishna c. Sep 02, 2018

Very good book

Deep Learning with PyTorch: A practical approach to building neural network models using PyTorch

What do you get with eBook?

Deep Learning with PyTorch

Building Blocks of Neural Networks

Installing PyTorch

Our first neural network

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