Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
List of all the lessons learned, best practices, and links from my time studying machine learning
| Date | Stars |
|---|---|
| 2026-07-31 | 1009 |
| 2026-08-06 | 1009 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Machine Learning Links and Lessons Learned List of all the lessons learned, best practices, and links from my time studying machine learning. * [Learning Machine Learning](#learning-machine-learning) * [Best Courses](#best-courses) * [Most Important Deep Learning Papers](#most-important-deep-learning-papers) * [Cool Use Cases of ML](#cool-use-cases-of-ml) * [ML Tech Talks](#ml-tech-talks) * [Best Blogs](#best-blogs) * [Data and Features](#data-and-features) * [Models](#models) * [Hyperparameters](#hyperparameters) * [Tensorflow/Deep Nets](#tensorflowdeep-nets) * [Deep Learning Frameworks](#deep-learning-frameworks) * [CNNs](#cnns) * [NLP](#nlp) * [Deep Reinforcement Learning](#deep-reinforcement-learning) * [ML Project Advice](#ml-project-advice) * [Math Resources](#math-resources) * [Bias in Machine Learning](#bias-in-machine-learning) * [Kaggle](#kaggle) * [Debugging ML Models](#debugging-ml-models) * [Best Python Libraries for ML](#best-python-libraries-for-ml) * [Other Interesting Links](#other-interesting-links) * [UCLA ACM AI Resources](#ucla-acm-ai-resources) * [Random Thoughts](#random-thoughts) * [Research Ideas](#research-ideas) * [Other](#other) ## Learning Machine Learning "How do you get started with machine learning?". With AI and ML becoming such huge words in the tech industry, it's hard to go a full week without hearing something along these lines on online forums, in discussions with other students at UCLA, and even from fellow pre-meds and humanities majors. From my own experience of getting familiar with ML and from my experiences of teaching others through ACM AI, here's my best response to that question. 1) Before getting started with any code or any technical terms, I think the best first step is to gain a big picture understanding of what machine learning is, and what it is attempting to do. When first teaching other students about ML, I've found that it's **very** important to give them a general understanding of the field before starting to dive into terms like gradient descent and loss function. Machine learning, as stated by Wikipedia, is a *field of computer science that gives computers the ability to learn without being explicitly programmed*. I might also add that machine learning is a subfield of AI, and that it is a unique approach to creating intelligent systems by making use of training data and optimization. I'd recommend the following high level links and videos to get you comfortable with the field as a whole. * [Machine Learning Introduction](https://www.youtube.com/watch?v=seG9J49bBYI): Loved this video because it starts with great definitions and introduces you to important terminology. Feel free to stop at 6:47. * [What is Machine Learning?](https://www.youtube.com/watch?v=WXHM_i-fgGo): Explains the 3 different subareas of machine learning: supervised learning, unsupervised learning, reinforcement learning * [A Friendly Intro to Machine Learning](https://www.youtube.com/watch?v=IpGxLWOIZy4): Great video with some cool illustrations, but honestly I think just watching until 5:54 is sufficient. * [Basic Machine Learning Algorithms Overview](https://www.youtube.com/watch?v=ggIk08PNcBo): Don't worry about knowing exactly what every one of these terms mean. Just get a sense for the different algorithms and the tasks they are used for. We'll go into way more detail later on. * [Machine Learning from Zero to Hero](https://medium.freecodecamp.org/machine-learning-how-to-go-from-zero-to-hero-40e26f8aa6da): Get motivated to learn Machine Learning. Sometimes a simple sitting back and watching the right videos can ignite and fan the fire to get active in ML. If you know software, this is a great starting post. Don't worry about knowing every single detail in each of the videos, but rather think about the high level goal of ML. 2) Okay cool, so now you should have a general idea of the goal of machine learning. We want to be able to create a system that is able to perform some
Excerpt of 57,876 characters
Read on GitHub
Adit Deshpande · Verse Medical · United States
85
Gant Laborde · Infinite Red
3
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a9e7aba78305b00c, llm:Repository description: "List of all the lessons learned, best practices, and links from my time studying machine learning"; topic: machine-learning