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Deep Learning with PyTorch
Deep Learning with PyTorch

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

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Profile Icon Vishnu Subramanian
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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.4 (13 Ratings)
Paperback Feb 2018 262 pages 1st Edition
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Arrow left icon
Profile Icon Vishnu Subramanian
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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.4 (13 Ratings)
Paperback Feb 2018 262 pages 1st Edition
eBook
zł59.99 zł141.99
Paperback
zł177.99
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Free Trial
eBook
zł59.99 zł141.99
Paperback
zł177.99
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Deep Learning with PyTorch

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

Installing PyTorch

PyTorch is available as a Python package and you can either use pip, or conda, to build it or you can build it from source. The recommended approach for this book is to use the Anaconda Python 3 distribution. To install Anaconda, please refer to the Anaconda official documentation at https://conda.io/docs/user-guide/install/index.html. All the examples will be available as Jupyter Notebooks in the book's GitHub repository. I would strongly recommend you use Jupyter Notebook, since it allows you to experiment interactively. If you already have Anaconda Python installed, then you can proceed with the following steps for PyTorch installation.

For GPU-based installation with Cuda 8:

conda install pytorch torchvision cuda80 -c soumith

For GPU-based installation with Cuda 7.5:

conda install pytorch torchvision -c soumith

For non-GPU-based installation:

conda...

Our first neural network

We present our first neural network, which learns how to map training examples (input array) to targets (output array). Let's assume that we work for one of the largest online companies, Wondermovies, which serves videos on demand. Our training dataset contains a feature that represents the average hours spent by users watching movies on the platform and we would like to predict how much time each user would spend on the platform in the coming week. It's just an imaginary use case, don't think too much about it. Some of the high-level activities for building such a solution are as follows:

  • Data preparation: The get_data function prepares the tensors (arrays) containing input and output data
  • Creating learnable parameters: The get_weights function provides us with tensors containing random values that we will optimize to solve our problem...

Summary

In this chapter, we explored various data structures and operations provided by PyTorch. We implemented several components, using the fundamental blocks of PyTorch. For our data preparation, we created the tensors used by our algorithm. Our network architecture was a model for learning to predict average hours spent by users on our Wondermovies platform. We used the loss function to check the standard of our model and used the optimize function to adjust the learnable parameters of our model to make it perform better.

We also looked at how PyTorch makes it easier to create data pipelines by abstracting away several complexities that would require us to parallelize and augment data.

In the next chapter, we will dive deep into how neural networks and deep learning algorithms work. We will explore various PyTorch built-in modules for building network architectures, loss...

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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

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Publication date : Feb 23, 2018
Length: 262 pages
Edition : 1st
Language : English
ISBN-13 : 9781788624336
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ISBN-13 : 9781788624336
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Table of Contents

10 Chapters
Getting Started with Deep Learning Using PyTorch Chevron down icon Chevron up icon
Building Blocks of Neural Networks Chevron down icon Chevron up icon
Diving Deep into Neural Networks Chevron down icon Chevron up icon
Fundamentals of Machine Learning Chevron down icon Chevron up icon
Deep Learning for Computer Vision Chevron down icon Chevron up icon
Deep Learning with Sequence Data and Text Chevron down icon Chevron up icon
Generative Networks Chevron down icon Chevron up icon
Modern Network Architectures Chevron down icon Chevron up icon
What Next? Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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P. Romero Jun 11, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
J K Mar 04, 2019
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A very good book for startersHands on training examples are good.The book is must have for deep learning enthusiasts
Amazon Verified review Amazon
archit gupta Nov 01, 2018
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Nice book
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Sumesh Oct 21, 2018
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Very good book for beginners.
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krishna c. Sep 02, 2018
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Very good book
Amazon Verified review Amazon
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