Deep Learning with PyTorch Quick Start Guide, published by Packt
This is the code repository for Deep Learning with PyTorch Quick Start Guide, published by Packt.
Learn to train and deploy neural network models in Python
PyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power.
This book covers the following exciting features:
- Set up the deep learning environment using the PyTorch library
- Learn to build a deep learning model for image classification
- Use a convolutional neural network for transfer learning
- Understand to use PyTorch for natural language processing
- Use a recurrent neural network to classify text
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
import numpy as np
x = np.array([[1,2,3],[4,5,6],[1,2,5]])
y = np.linalg.inv(x)
print (y)
print (np.dot(x,y))
Following is what you need for this book: Developers and Data Scientist familiar with Machine Learning but new to deep learning, or existing practitioners of deep learning who would like to use PyTorch to train their deep learning models will find this book to be useful. Having knowledge of Python programming will be an added advantage, while previous exposure to PyTorch is not needed.
With the following software and hardware list you can run all code files present in the book (Chapter 1-6).
| Chapter | Software required | OS required |
|---|---|---|
| 1-6 | Anaconda distribution of Python 3.x, | Windows, Mac OS X, and Linux (Any) |
| PyTorch 0.4 |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
David Julian is a freelance technology consultant and educator. He has worked as a consultant for government, private, and community organizations on a variety of projects, including using machine learning to detect insect outbreaks in controlled agricultural environments (Urban Ecological Systems Ltd., Bluesmart Farms), designing and implementing event management data systems (Sustainable Industry Expo, Lismore City Council), and designing multimedia interactive installations (Adelaide University). He has also written Designing Machine Learning Systems With Python for Packt Publishing and was a technical reviewer for Python Machine Learning and Hands-On Data Structures and Algorithms with Python - Second Edition, published by Packt.
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