📘 An automatically and constantly up-to-date collection of the best ML resources by topic, curated by the community. This is a dynamic and community-driven solution to static and out-dated awesome-lists. Click on any topic to discover the best Tutorials and Libraries to help you build.
👉 https://madewithml.com/topics/
Most topic pages are arranged for an optimal learning experience, as opposed to overwhelming you with a massive list of resources. They come with the following sections:
- Overview (intuition)
- Tutorials (code)
- Libraries (by application)
And we cover the follow categories of topics:
- Frameworks (Python, TensorFlow, PyTorch, JAX, etc.)
- Data (augmentation, preprocessing, versioning, etc.)
- Algorithms (regression, CNNs, Transformers, GANs, etc.)
- Tasks (text classification, object detection, recsys, etc.)
- Modeling (pretraining, interpretability, compression, etc.)
- End-to-end (production, serving, testing, CI/CD, etc.)
- Industries (health, more coming soon)
- Collections (checklists, courses, interactive, etc.)
ℹ️ This page is for the best resources of all time by topic. If you're looking for trending content of the day, check out the home page. And if you don't see a specific topic here, you can search for it using the search bar at the top.
Made With ML is a vibrant community where people learn, explore and build with ML.
Stay on top of the daily influx of ML content and focus only on community-curated high quality content. See what the community has to say: https://madewithml.com/about/#feedback
Discover the best tutorials and libraries (tagged and community-curated) to use for your next project. https://madewithml.com/topics/
Interact directly with the authors (questions, thank yous and even mentorship). See examples here: https://twitter.com/madewithml/status/1286529443352023041
Showcase your dynamic portfolio. Check out some of the amazing portfolios here: https://twitter.com/madewithml/status/1284503478685978625
💬 Join the discussions on our community Slack channel and follow us on Twitter and LinkedIn for daily trending content and YouTube (lessons coming soon)!
Top comments (2)
Thanks for this resource! I have a speaker coming to talk machine learning at a free virtual meetup next week on August 14. I've seen this presentation and it's a lot of fun. He walks through how the gradient descent algorithm works for machine learning and actually codes a sample using it in JavaScript. cfe.dev/events/how-machine-learnin...
I thought this might be a good resource for anyone interested in the topic (it will be recorded as well). Also would love if you'd be willing to share it to help spread the word.
Salam , thanks helpfull