# Machine Learning * **We have a $50 prize for anyone who builds a *****really***** cool ML project. T**he prize may not go to anyone or may go to multiple people. You can seek out Vihaan Sondhi, Kyle Lee, and/or Dean Chen at the event to see if your project idea might qualify or email one of them in advance at first\_name dot last\_name @teenhacksli.com. * [Fast.ai](https://docs.fast.ai/tutorial.html) is definitely a good choice for a library if you're just getting started with machine learning. * [Kaggle](https://www.kaggle.com/) competitions are great for finding datasets and getting ideas for things to work on. * Image Classification * Fast.ai is definitely what you want to use here. Look through their tutorials to get an idea. * Language Modeling (aka generating text) * Learn using Andrej Karpathy's [great introductory course](https://karpathy.ai/zero-to-hero.html). It goes from scratch to building a transformer. Extending on any of these significantly and/or implementing all of the coding exercises would be a valid project that would be both fun and an incredible learning experience. * Reinforcement Learning * The following two resources are great for both learning and finding ideas for projects to work on. * [https://arena3-chapter2-rl.streamlit.app/](https://arena3-chapter2-rl.streamlit.app/%5B2.2%5D_Q-Learning_and_DQN) -> if you're already comfortable with rl, look at the end of each section for bonus exercises if you want ideas. * -> implement algorithms from the canonical tabular RL textbook. * General ideas if you already have experience: * Get started on an autodiff library like [this](https://github.com/UlisseMini/light) * Implement a neural net class from scratch in Numpy. Include normalization (batch norm, layer norm), gradient clipping, lr-scheduling, dropout, several different optimizers, activation functions, and more. * For relatively cheap GPU rental, check out [Vast.ai](https://vast.ai/) and [Lambda Labs](https://lambdalabs.com/), but Google Colab should be sufficient for most projects. * If you anticipate that paying for GPU rental will be a bottleneck for you, email us at (cc ) and we'll see if we can help you out. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://vihaans-cp-notes.gitbook.io/teenhacks-long-island-quickstart-guide/projects/machine-learning.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.