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Production-Ready Applied Deep Learning

You're reading from   Production-Ready Applied Deep Learning Learn how to construct and deploy complex models in PyTorch and TensorFlow deep learning frameworks

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781803243665
Length 322 pages
Edition 1st Edition
Tools
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Authors (3):
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Lenin Mookiah Lenin Mookiah
Author Profile Icon Lenin Mookiah
Lenin Mookiah
Tomasz Palczewski Tomasz Palczewski
Author Profile Icon Tomasz Palczewski
Tomasz Palczewski
Jaejun (Brandon) Lee Jaejun (Brandon) Lee
Author Profile Icon Jaejun (Brandon) Lee
Jaejun (Brandon) Lee
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1 – Building a Minimum Viable Product
2. Chapter 1: Effective Planning of Deep Learning-Driven Projects FREE CHAPTER 3. Chapter 2: Data Preparation for Deep Learning Projects 4. Chapter 3: Developing a Powerful Deep Learning Model 5. Chapter 4: Experiment Tracking, Model Management, and Dataset Versioning 6. Part 2 – Building a Fully Featured Product
7. Chapter 5: Data Preparation in the Cloud 8. Chapter 6: Efficient Model Training 9. Chapter 7: Revealing the Secret of Deep Learning Models 10. Part 3 – Deployment and Maintenance
11. Chapter 8: Simplifying Deep Learning Model Deployment 12. Chapter 9: Scaling a Deep Learning Pipeline 13. Chapter 10: Improving Inference Efficiency 14. Chapter 11: Deep Learning on Mobile Devices 15. Chapter 12: Monitoring Deep Learning Endpoints in Production 16. Chapter 13: Reviewing the Completed Deep Learning Project 17. Index 18. Other Books You May Enjoy

Implementing and training a model in PyTorch

PyTorch is a Python library for Torch, a ML package for Lua. The main features of PyTorch include graphics processing unit- (GPU-) accelerated matrix calculation and automatic differentiation for building and training neural networks. Creating the computation graph dynamically as the code gets executed, PyTorch is gaining popularity for its flexibility and ease of use, as well as its efficiency in model training.

Built on top of PyTorch, PyTorch Lightning (PL) provides another layer of abstraction, hiding many boilerplate codes. The new framework pays more attention to researchers by decoupling research-related components of PyTorch from the engineering-related components. PL codes are typically more scalable and easier to read than PyTorch codes. Even though the code snippets in this book put more emphasis on PL, PyTorch and PL share a lot of functionalities, so most components are interchangeable. If you are willing to dig into the...

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