fastai

Implementation for the fast.ai deep learning curriculum. This repository tracks my progress through the following courses:

1. Practical Deep Learning for Coders
2. Deep Learning Foundations to Stable Diffusion

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1. Practical Deep Learning for Coders

Hands-on application of deep learning using fastai and PyTorch.

Chapter	Notebook / Resource	Description
01	Intro to Deep Learning	Basics of the fastai library
	Is it a bird?	Creating a model from custom data
02	Production	Deploying models and Hugging Face Spaces
03	How a Neural Net works	Understanding the underlying math
	Image Model Comparison	Comparing different architectures
04	MNIST Basics	Building the "Hello World" of CV
	NLP for Beginners	Intro to Natural Language Processing
05	Random Forests	Tabular data and decision trees
	Linear Model from Scratch	Re-implementing the core logic
	Why use a Framework?	The benefits of high-level abstractions
06	Paddy Disease Part 1	Large scale image classification
07	Collaborative Filtering	Recommendation systems
	Road to the Top Part 4	Multi-target modeling

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2. Deep Learning Foundations to Stable Diffusion

This section covers the math and architecture behind generative models and the latest stable diffusion techniques.

In progress...

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License

This repository is licensed under the MIT License. See the LICENSE file for more details.