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The Artificial Intelligence Infrastructure Workshop

You're reading from   The Artificial Intelligence Infrastructure Workshop Build your own highly scalable and robust data storage systems that can support a variety of cutting-edge AI applications

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781800209848
Length 732 pages
Edition 1st Edition
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Authors (6):
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Bas Geerdink Bas Geerdink
Author Profile Icon Bas Geerdink
Bas Geerdink
Chinmay Arankalle Chinmay Arankalle
Author Profile Icon Chinmay Arankalle
Chinmay Arankalle
Kunal Gera Kunal Gera
Author Profile Icon Kunal Gera
Kunal Gera
Kevin Liao Kevin Liao
Author Profile Icon Kevin Liao
Kevin Liao
Gareth Dwyer Gareth Dwyer
Author Profile Icon Gareth Dwyer
Gareth Dwyer
Anand N.S. Anand N.S.
Author Profile Icon Anand N.S.
Anand N.S.
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Table of Contents (14) Chapters Close

Preface
1. Data Storage Fundamentals 2. Artificial Intelligence Storage Requirements FREE CHAPTER 3. Data Preparation 4. The Ethics of AI Data Storage 5. Data Stores: SQL and NoSQL Databases 6. Big Data File Formats 7. Introduction to Analytics Engine (Spark) for Big Data 8. Data System Design Examples 9. Workflow Management for AI 10. Introduction to Data Storage on Cloud Services (AWS) 11. Building an Artificial Intelligence Algorithm 12. Productionizing Your AI Applications Appendix

Getting Started with PyTorch

PyTorch is one of the most popular open-source deep learning libraries in the world right now. It's known for its fast iteration, model ideation, and prototyping. As a result, many AI researchers or engineers implement their state-of-the-art deep learning models through the PyTorch library or its ecosystem. PyTorch has a large machine learning community and its community continues to grow and mature. Another popular deep learning framework is TensorFlow. TensorFlow gained its popularity a little earlier than PyTorch. Let's compare the differences in their core features:

Figure 11.10: PyTorch versus TensorFlow

Generally speaking, PyTorch is more development-friendly and TensorFlow is more deployment-friendly. Both are very powerful deep learning frameworks. If you want a better development and research experience, PyTorch is a better fit for you. On the other hand, if you want to deploy your models to production, then TensorFlow...

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