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Mastering PyTorch

You're reading from  Mastering PyTorch

Product type Book
Published in Feb 2021
Publisher Packt
ISBN-13 9781789614381
Pages 450 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Ashish Ranjan Jha Ashish Ranjan Jha
Profile icon Ashish Ranjan Jha
LinkedIn
Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), a master's degree in Computer Science from EPFL (Switzerland), and an MBA degree from Quantic School of Business (Washington). He has received a distinction in all 3 of his degrees. He has worked for large technology companies, including Oracle and Sony as well as the more recent tech unicorns such as Revolut, mostly focused on artificial intelligence. He currently works as a machine learning engineer. Ashish has worked on a range of products and projects, from developing an app that uses sensor data to predict the mode of transport to detecting fraud in car damage insurance claims. Besides being an author, machine learning engineer, and data scientist, he also blogs frequently on his personal blog site about the latest research and engineering topics around machine learning.
See other products by Ashish Ranjan Jha

Table of Contents (20) Chapters

Preface 1. Section 1: PyTorch Overview
2. Chapter 1: Overview of Deep Learning using PyTorch 3. Chapter 2: Combining CNNs and LSTMs 4. Section 2: Working with Advanced Neural Network Architectures
5. Chapter 3: Deep CNN Architectures 6. Chapter 4: Deep Recurrent Model Architectures 7. Chapter 5: Hybrid Advanced Models 8. Section 3: Generative Models and Deep Reinforcement Learning
9. Chapter 6: Music and Text Generation with PyTorch 10. Chapter 7: Neural Style Transfer 11. Chapter 8: Deep Convolutional GANs 12. Chapter 9: Deep Reinforcement Learning 13. Section 4: PyTorch in Production Systems
14. Chapter 10: Operationalizing PyTorch Models into Production 15. Chapter 11: Distributed Training 16. Chapter 12: PyTorch and AutoML 17. Chapter 13: PyTorch and Explainable AI 18. Chapter 14: Rapid Prototyping with PyTorch 19. Other Books You May Enjoy

Technical requirements

We will be using Jupyter Notebooks for all of our exercises. Here is a list of the Python libraries that will be installed for this chapter, using pip (for example, by running pip install torch==1.7.0 on the command line):

jupyter==1.0.0
torch==1.7.0
torchvision==0.8.1
torchviz==0.0.1
autoPyTorch==0.0.2
configspace==0.4.12
git+https://github.com/shukon/HpBandSter.git
optuna==2.2.0

Note

Auto-PyTorch is fully supported in Linux and macOS at the time of writing. However, Windows users might encounter issues while installing the library. It is therefore recommended to use macOS or Linux for working on Auto-PyTorch.

All code files relevant to this chapter are available at the following GitHub page: https://github.com/PacktPublishing/Mastering-PyTorch/tree/master/Chapter12.

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Authors (1)

Profile icon Ashish Ranjan Jha
LinkedIn
Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), a master's degree in Computer Science from EPFL (Switzerland), and an MBA degree from Quantic School of Business (Washington). He has received a distinction in all 3 of his degrees. He has worked for large technology companies, including Oracle and Sony as well as the more recent tech unicorns such as Revolut, mostly focused on artificial intelligence. He currently works as a machine learning engineer. Ashish has worked on a range of products and projects, from developing an app that uses sensor data to predict the mode of transport to detecting fraud in car damage insurance claims. Besides being an author, machine learning engineer, and data scientist, he also blogs frequently on his personal blog site about the latest research and engineering topics around machine learning.
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See other products by Ashish Ranjan Jha

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