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About RetroFed
About this forum

Welcome! This is a place for people to learn more about machine learning techniques, discuss applications and ask questions.

Example questions:

  • "Should I use a deep neural network for my audio classification task?"
  • "I'm working with a small dataset, what can I do to make my model generalize well?"
  • "Is there a library available that implements function X in language Y?"
  • "I want to learn more about the math behind machine learning technique A, where should I start?"

Please do:

  • Be kind to new people
  • Post guides and tutorials that you find helpful
  • Link to open/free sources instead of paywalled when possible

Please don't:

  • Post news articles / memes (there are other machine learning/AI communities for this)

Other communities in this area:

Similar subreddits: r/MLquestions, r/askmachinelearning, r/learnmachinelearning

2 members · 39 topics · 0 posts

RetroFed

Welcome to the nostalgia-filled piefed instance of yesteryear.

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If you have a question or comment you can post in !meta@retrofed.com or message @DosDude@retrofed.com or @Die4Ever@retrofed.com

In case of downtime and/or emergency, we will post updates on @DosDude@mastodon.social

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  • Be civil. Bigotry is not welcome. This includes (but is not limited to): transphobia, queerphobia, racism, ageism and ableism. We do not promote hate.
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74 registered users
Administrators: DosDude, Die4Ever
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Learn Machine Learning !learnmachinelearning@sh.itjust.works
Moderators: ShadowAether, zappy
New Topic RetroFed Forum Forum Index » Learn Machine Learning
Page 1 of 2   Goto page 1, 2 Last »
 Topics  Replies  Author  Score  Last Post 
Currently experimenting with exploration policies for deep RL on Super Mario Bros - Agent is able to beat all levels I threw at it - You can watch the AI learn live (www.twitch.tv) 0 pcouy 3 »
Google open sources tools to support AI model development (techcrunch.com) 0 ylai 1 »
Finetune LLMs on your own consumer hardware using tools from PyTorch and Hugging Face ecosystem (pytorch.org) 0 ylai 1 »
Understanding GPU Memory 2: Finding and Removing Reference Cycles (pytorch.org) 0 ylai 1 »
PyTorch: Compiling NumPy code into C++ or CUDA via torch.compile (pytorch.org) 0 ylai 1 »
Introduction to Kernel Methods for Machine Learning (seis.bristol.ac.uk) 0 ShadowAether 1 »
The Kernel Cookbook: Advice on Covariance functions (www.cs.toronto.edu) 0 ShadowAether 1 »
An Intuitive Tutorial to Gaussian Processes Regression (arxiv.org) 0 ShadowAether 1 »
[Resource] Understanding UMAP - Google PAIR (pair-code.github.io) 0 ShadowAether 1 »
[Resource] MIT OpenCourseWare: Introduction To Machine Learning (ocw.mit.edu) 0 ShadowAether 1 »
[Resource] Style Guide for Python Code: PEP 8 (peps.python.org) 0 ShadowAether 1 »
[Resource] MIT OpenCourseWare: Statistical Learning Theory (ocw.mit.edu) 0 ShadowAether 1 »
[Resource] MIT OpenCourseWare: Mathematics Of Machine Learning (ocw.mit.edu) 0 ShadowAether 1 »
[Resource] Durham University Materials for COMP3547 (Deep Learning) and COMP3667 (Reinforcement Learning) from Dr. Robert Lieck (github.com) 0 ShadowAether 1 »
[Resource] Rules of Machine Learning from Google (developers.google.com) 0 ShadowAether 1 »
[Resource] Coding Practices for Python/ML (github.com) 0 ShadowAether 1 »
[Resource] Tutorial: Image Recognition with CNN in Matlab (hevpdd.ca) 0 ShadowAether 1 »
[Resource] Tutorial: State of Charge Estimation with EKF and SVSF in Matlab (hevpdd.ca) 0 ShadowAether 1 »
[Resource] Standford University Cheat Sheets for ML (web version) (stanford.edu) 0 ShadowAether 1 »
Mathematics for Neural Networks (sh.itjust.works) 0 ShadowAether 1 »
[Resource] Materials from CORNELL CS4780/CS5780: Machine Learning for Intelligent Systems 0 ShadowAether 1 »
K-Means Clustering Infographic (sh.itjust.works) 0 ShadowAether 1 »
Classification Model Evaluation Metrics (www.researchgate.net) 0 ShadowAether 1 »
Introduction to Domain Adaptation for Neural Networks (machinelearning.apple.com) 0 ShadowAether 1 »
The standardization fallacy: the importance of variance (www.nature.com) 0 ShadowAether 1 »
New Topic Page 1 of 2   Goto page 1, 2 Last »

About this forum

Welcome! This is a place for people to learn more about machine learning techniques, discuss applications and ask questions.

Example questions:

  • "Should I use a deep neural network for my audio classification task?"
  • "I'm working with a small dataset, what can I do to make my model generalize well?"
  • "Is there a library available that implements function X in language Y?"
  • "I want to learn more about the math behind machine learning technique A, where should I start?"

Please do:

  • Be kind to new people
  • Post guides and tutorials that you find helpful
  • Link to open/free sources instead of paywalled when possible

Please don't:

  • Post news articles / memes (there are other machine learning/AI communities for this)

Other communities in this area:

Similar subreddits: r/MLquestions, r/askmachinelearning, r/learnmachinelearning

2 members · 39 topics · 0 posts
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