start-machine-learning
ABigSurvey
| start-machine-learning | ABigSurvey | |
|---|---|---|
| 45 | 1 | |
| 4,115 | 1,951 | |
| - | 1.5% | |
| 5.8 | 3.8 | |
| 21 days ago | about 1 month ago | |
| MIT License | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
start-machine-learning
- Can anyone provide me a roadmap for learning AI and Data science?
-
Is it Possible to Work in AI without a Formal Degree in Computer Science?
Yes, I mean the second. What technologies do you use and what's your mansion? Then I found this repository on GitHub https://github.com/louisfb01/start-machine-learning, should it be a good roadmap to follow in your opinion?
-
Suggestions for my future(Long Post)
Someone posted this a year ago but must have updated it for 2023.
-
I want to learn Artificial Intelligence from beginning.
AI learning roadmap
-
Start learning the basics of AI and ML now.
For anyone wanting to dive and have no idea where to start, someone posted this to a programming sub a while ago and I bookmarked it.
-
Any path to learn?
Hi, we have a guide in our community we share for this full of free learning resources online: https://github.com/louisfb01/start-machine-learning
-
A friendly approach to ML (2022-just updated)!
The guide: https://www.louisbouchard.ai/learnai/ GitHub repo: https://github.com/louisfb01/start-machine-learning
- A Complete Roadmap for learning Machine Learning with many valuable resources + staying up-to-date with the news. Intended for anyone having zero or a small background in programming, maths, and machine learning.
ABigSurvey
-
Becoming a ML/DL researcher
Find out a couple of survey papers (e.g., https://github.com/NiuTrans/ABigSurvey) and start reading them. Don't go into the details at first. Develop a broader picture - what is important, why is it important, is it worth applying ML to that topic, and most importantly if given that problem how would you approach it.
What are some alternatives?
coursera-deep-learning-specialization - Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
Awesome-Image-Inpainting - A curated list of image inpainting and video inpainting papers and resources
PES-2021-Cheat-Table - Cheat Table for eFootball PES 2021
Machine-Learning-Tutorials - machine learning and deep learning tutorials, articles and other resources
learn-monogame.github.io - Documentation to learn MonoGame from the ground up.
GNNPapers - Must-read papers on graph neural networks (GNN)
yt-channels-DS-AI-ML-CS - A comprehensive list of 180+ YouTube Channels for Data Science, Data Engineering, Machine Learning, Deep learning, Computer Science, programming, software engineering, etc.
audio-ai-timeline - A timeline of the latest AI models for audio generation, starting in 2023!
nexify.io - Develop your skills with programming courses, explained step by step, to learn by building things.
human-memory - Course materials for Dartmouth course: Human Memory (PSYC 51.09)
python-awesome - Learn Python, Easy to learn, Awesome
awesome-ai-residency - List of AI Residency Programs