A Hacker’s Starter Pack for AI & ML Hacks

So, you want to build a hack with Artificial Intelligence and Machine Learning. You’ve heard it’s all the rage and you’ve seen all the cool stuff DeepMind and OpenAI can do with algorithms learning from heaps of data. How do you do it yourself? Luckily, the internet has tons of resources to get you started.

First things first: most machine learning is done in Python, so if you haven’t touched that in a while, it’s probably a good idea to refresh your memory with a resource like LearnPython.

Alright, now we get to our first decision point. There are two strategies to incorporate AI & ML into your hack: you can build a neural network from scratch or you can use pre-trained networks from an API. If the thing you want to do is relatively simple, and lots of work has been done in that domain already, it is likely that a pre-trained model exists for your use-case. However, if the task you want your model to perform is complex or highly specific scenario, it is unlikely that a pre-trained network will do the job, and you’ll need to put together the pieces yourself. If you think there might be a pre-trained network that covers your use-case, check out the model zoo at https://modelzoo.co/, a website that shares open-source network architectures and pre-trained weights. Each model in the zoo will direct you to the git repo where that model can be found as well as instructions for running the model.

If your task is unique or you’re just really interested in how these neural networks work, here’s a list of resources to help get you started building your own:

• Andrej Karpathy’s Hacker’s Guide to Neural Networks: A great introductory guide to teach you the basics written by one of the masters. It contains detailed descriptions that help build an intuitive understanding of neural networks from the ground up.
• The Pytorch 60 Minute Blitz: This tutorial series from Pytorch (one of the main machine learning frameworks) exposes you to the basics with tons of example code and gives you everything you need to get started with the framework in just an hour (though account for a little extra time to install packages and get everything working).
• The Rest of the Pytorch Tutorials: Once you’ve hit all the basics with the 60 minute blitz, pick up one of their more challenging tutorials to learn more complex topics like neural style transfer!
• Tensorflow Tutorials: As an alternative to Pytorch, Tensorflow is another great machine learning framework. Check out these tutorials to get started with it!
• OpenAI’s Spinning Up in Deep Reinforcement Learning: If you want to create an agent to play a game or make decisions based on their environment, check out OpenAI’s extensive beginner’s guide to the field of reinforcement learning!

If you already have experience with machine learning and artificial intelligence, here are some projects from Stanford’s machine learning courses to serve as inspiration (look for the cool ideas you could iterate upon, or apply to a new domain):

• Artificial Intelligence (229):
• 2011 projects
• Natural Language Processing (224n):
• 2015 projects
• Computer Vision (231n):
• 2017 projects
• 2016 projects
• 2015 projects

Machine learning doesn’t work without data, and sometimes finding an interesting dataset can spark a great idea for a hack, so here is a list of resources to help you find applicable datasets:

• Kaggle: the one-stop-shop for all kinds of data science challenges and fun datasets.
• Google Dataset Search: Google’s tool for pulling the dataset you need from the depths of the interwebs.
• ImageNet: The famous (and massive) collection of labeled images used for all kinds of detection and classification tasks.
• UCI Machine Learning Dataset Repository: A collection of datasets with a very wide variety of problem domains.
• Stanford NLP Resource List: Sources of text data used by Stanford’s NLP lab

Finally, some miscellaneous resources to take a look at if you’re still looking for things to read:

• An Introduction to GANs: Learn how to create images without any labels.
• Awesome Deep Vision: “A curated list of deep learning resources for computer vision.”
• r/MachineLearning: Reddit’s machine learning community.
• r/learnmachinelearning: Reddit’s community of machine learning students
• The Deep Learning Book: This textbook on machine learning is a fantastic resource for those with a solid background in statistics and mathematics. It explains a lot of difficult concepts at very detailed levels. It probably is not a great resource for hackers to digest information quickly, but if you want to understand what is really going on under the hood and why all these bells and whistles actually work, read this.

Good luck and happy hacking!

Thanks to Jackson Waschura (Cal Poly CS 21’) for putting together this guide!