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Getting Started with Image Classification with PyTorch

Beginner's guide to implement efficient and high-performing code for image classification project using Pytorch and Python programming language.

Beginner

50 Lessons

6h

Certificate of Completion

Beginner's guide to implement efficient and high-performing code for image classification project using Pytorch and Python programming language.
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Explanations

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Explanations

This course includes

110 Playgrounds
6 Quizzes
Course Overview
What You'll Learn
Course Content
Apply Your Skills

Course Overview

PyTorch is a machine learning framework used in a wide array of popular applications, including Tesla’s Autopilot and Pyro, Uber’s probabilistic modeling engine. This course is an introduction to image classification using PyTorch’s computer vision models for training and tuning your own model. You’ll start with the fundamental concepts of applying machine learning and its applications to image classification before exploring the process of training your AI model. You’ll prepare data for intake by the comp...Show More
PyTorch is a machine learning framework used in a wide array of popular applications, including Tesla’s Autopilot and Pyro, Uber’s probabilistic modeling engine. This course is an introduction to image classification using PyTorch’s computer vision models...Show More

What You'll Learn

A basic overview of the PyTorch Image Model
The ability to fine-tune custom image classification models
A working knowledge of deploying models as REST API
A familiarity with converting PyTorch models into ONNX format
A basic overview of the PyTorch Image Model

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Course Content

1.

Introduction

4 Lessons

Get familiar with image classification, techniques, metrics, and PyTorch Image Model framework.

2.

Basic Concepts

8 Lessons

Look at essential PyTorch image classification, including models, datasets, preprocessing, and inference.

3.

Augmentation

7 Lessons

Examine augmentation techniques to diversify datasets, improve model performance, and mitigate overfitting.

4.

Loss

4 Lessons

Grasp the fundamentals of loss functions to improve model accuracy in PyTorch.

5.

Training

7 Lessons

Solve problems in image classification training using PyTorch, models, and techniques.

7.

Deployment

5 Lessons

Practice using FastAPI to deploy image classification models with HTTP methods and REST API integration.

8.

Appendix

5 Lessons

Learn how to use virtual environments, Python packages, training arguments, and deployment dependencies.

9.

Conclusion

1 Lessons

Solve challenges with essential PyTorch image classification concepts, model deployment, and performance improvement.

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