This repository contains my lab work and my final project for the MIT Introduction to Deep Learning course (MIT 6.S191). I took this class summer 2023 to self study AI and neural networks. My lab work solutions are in the labs folder. Official solutions can be found in the MIT repository for this class linked below. My final paper is saved as FINAL_PROJECT.pdf.
MIT repository for the class: https://github.com/aamini/introtodeeplearning/tree/master
My solutions are in the lab folder. They also include corrections to errors in the original code provided by MIT. Descriptions of the labs can be found below:
- Lab 1 (part 1): An introduction to simple concepts in tensorflow.
- Lab 1 (part 2): Music generation with a recurrent neural network. My favorite song I was able to generate with one of these models can be found in the labs folder.
- Lab 2 (part 1): MNIST Digit Classification
- Lab 2 (part 2): Diagnosing bias in facial detection systems
- Lab 3 (part 1): An introduction to capsa as a way to evaluate uncertainty and bias.
- Lab 3 (part 2): This lab couldn't be completed because the dependency capsa is no longer available to install.
The final project for this class was to create a proposal for a new idea in AI. The original format of the class was 3 minute live presentations but I decided to write a short paper instead. My work is attached as FINAL_PROJECT.pdf in this repository.
All code in the labs folder is based off of assignments provided in the MIT Introduction to Deep Learning Github repository, where you can find alternative assignment solutions: https://github.com/aamini/introtodeeplearning/tree/master
The code in the MIT Introduction to Deep Learning is copyright 2023 and as a result, further use of the code in the repository cannot be used in conflict with the license. A copy of the applicable license can be found in the labs folder.
© MIT Introduction to Deep Learning
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