Introduction to PyTorch image classification-01.png

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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 computer vision model with image pre-processing, set up pipelines for training your model, and fine-tune the variables to improve predictive performance. You’ll finish by deploying the image classification model by converting to ONNX format and serving it via REST API.

By the end of this course, you’ll be able to build and deploy your own image classification models from scratch.

Getting Started with Image Classification with PyTorch

**FastAPI** is a Python web framework for building RESTful APIs. It strictly adheres to the OpenAPI specification for API creation.

At its core, it provides the following features:
* Automatic documentation with JSON schema.
* Data validation using Python-style type annotations.
* In-process background tasks.
* Startup and shutdown events for the initialization and termination of processes.
* Support for WebSockets.
* Support for dependency injections.
* Support for HTTP Basic, OAuth2, JSON Web Token (JWT), and more.

## Automatic documentation
There are two types of documentation available in FastAPI:
1. **SwaggerUI**: A single-page interactive documentation. We can test our API directly from the browser.





**FastAPI** is a Python web framework for building RESTful APIs. It strictly adheres to the OpenAPI specification for API creation.

At its core, it provides the following features:
* Automatic documentation with JSON schema.
* Data validation using Python-style type annotations.
* In-process background tasks.
* Startup and shutdown events for the initialization and termination of processes.
* Support for WebSockets.
* Support for dependency injections.
* Support for HTTP Basic, OAuth2, JSON Web Token (JWT), and more.

# Automatic documentation
There are two types of documentation available in FastAPI:
1. **SwaggerUI**: A single-page interactive documentation. We can test our API directly from the browser.





Explore the fundamental concepts of FastAPI.

Introduction

Basic Concepts

Augmentation

Loss

Training

Model Conversion

Deployment

Appendix

Conclusion

Overview of FastAPI

Automatic documentation