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

Product type Paperback

Published in Aug 2022

Publisher Packt

ISBN-13 9781803243665

Length 322 pages

Edition 1st Edition

Tools

PyTorch

Concepts

Deep Learning

Authors (3):

Lenin Mookiah

Tomasz Palczewski

Jaejun (Brandon) Lee

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Table of Contents (19) Chapters

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

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18. Other Books You May Enjoy

Setting up notebook environments

Python is one of the most popular programming languages that’s widely used for data analysis. Its advantage comes from dynamic typing and being compile-free. With its flexibility, it has become the language that data scientists use the most. In this section, we will introduce how to set up a Python environment for a DL project using Anaconda and Preferred Installer Program (PIP). These tools allow you to create a distinct environment for every project while simplifying package management. Anaconda provides a desktop application with a GUI called Anaconda Navigator. We will walk you through how to set up a Python environment and install popular Python libraries for DL projects such as TensorFlow, PyTorch, NumPy, pandas, scikit-learn, Matplotlib, and Seaborn.

Setting up a Python environment

Python can be installed from www.python.org/downloads. However, Python versions are often available through package managers that are provided by the...

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

Table of Contents (19) Chapters

Setting up notebook environments

Setting up a Python environment

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Authors (3)

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