Programming PyTorch for Deep Learning

Creating and Deploying Deep Learning Applications

Paperback Engels 2019 1e druk 9781492045359
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Samenvatting

Deep learning is changing everything. This machine learning method has already surpassed traditional computer vision techniques, and the same is happening with NLP. If you're looking to bring deep learning into your domain, this practical book will bring you up to speed on key concepts using Facebook's PyTorch framework.

Once author Ian Pointer helps you set up PyTorch on a cloud-based environment, you'll learn how use the framework to create neural architectures for performing operations on images, sound, text, and other types of data. By the end of the book, you'll be able to create neural networks and train them on multiple types of data.

- Learn how to deploy deep learning models to production
- Explore PyTorch use cases in companies other than Facebook
- Learn how to apply transfer learning to images
- Apply cutting-edge NLP techniques using a model trained on Wikipedia

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Over Ian Pointer

Currently Ian is the Director of Partner Engineering at a company called Kogentix that specializes in Machine Learning solutions (including Deep Learning techniques), with multiple Fortune 100 clients. Prior to that, he worked for many years at an early Big Data startup called Mammoth Data, cutting his teeth on Apache Hadoop and Apache Spark. He emigrated to the US from the UK in 2011 and became an American citizen in 2017.

Andere boeken door Ian Pointer

Inhoudsopgave

Preface
Deep Learning in the World Today
But What Is Deep Learning Exactly, and Do I Need a PhD to Understand It?
PyTorch
What About TensorFlow?
Conventions Used in This Book
Using Code Examples
O’Reilly Online Learning
How to Contact Us
Acknowledgments

1. Getting Started with PyTorch
Building a Custom Deep Learning Machine
GPU
CPU/Motherboard
RAM
Storage
Deep Learning in the Cloud
Google Colaboratory
Cloud Providers
Which Cloud Provider Should I Use?
Using Jupyter Notebook
Installing PyTorch from Scratch
Download CUDA
Anaconda
Finally, PyTorch! (and Jupyter Notebook)
Tensors
Tensor Operations
Tensor Broadcasting
Conclusion
Further Reading

2. Image Classification with PyTorch
Our Classification Problem
Traditional Challenges
But First, Data
PyTorch and Data Loaders
Building a Training Dataset
Building Validation and Test Datasets
Finally, a Neural Network!
Activation Functions
Creating a Network
Loss Functions
Optimizing
Training
Making It Work on the GPU
Putting It All Together
Making Predictions
Model Saving
Conclusion
Further Reading

3. Convolutional Neural Networks
Our First Convolutional Model
Convolutions
Pooling
Dropout
History of CNN Architectures
AlexNet
Inception/GoogLeNet
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          Programming PyTorch for Deep Learning