Hey everyone, your average AI student here. As I suppose if you are reading this is because you have an interest in learning about AI, but for someone who is totally new to the subject or with previous knowledge the amount of variations and paths can be a bit confusing.
The first thing to do is to have a specific focus on where to aim your studies, being two possible paths quite simplified:
Use models already created for specific utilities 2.
Create models
As I said before these two paths are quite simplified and contain several modifications, for example in path 1, you have LLM, Langchain, Deep Learning and Machine Learning to name a few. But in path 2 you also have the same but with other approaches.
Well, after this introduction how do we approach the study? The first thing would be to identify the target, once we have identified the target we move on to investigate the ramifications and little by little we enter the study.
Learning the definitions and basic knowledge in the field is necessary, no matter what your objective is, knowledge always helps to learn more.
Programming is also necessary C## or Pytorch depending the model.
With this I hope to have made clear a basis of how to approach the study of AI in 2024, then I leave a couple of useful links for the study.
https://huggingface.co -- Models and documents
https://arxiv.org/pdf/2312.00752.pdf -- Mamba study
https://course.fast.ai -- AI introduction course
https://github.com/oobabooga/text-generation-webui -- A great LLM introduction
https://towardsdatascience.com/navigating-the-ai-landscape-of-2024-trends-predictions-and-possibilities-41e0ac83d68f -- Information about AI's 2024
https://www.verses.ai -- An interesting project
https://paperswithcode.com -- Practices
https://www.coursera.org/learn/introduction-to-generative-ai -- Course
https://www.futuretools.io -- Course
https://teachablemachine.withgoogle.com -- Couse
https://www.langchain.com -- Langchain info
https://spinningup.openai.com/en/latest/user/introduction.html -- Useful info
http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ -- ML introduction
https://a16z.com/ai-canon/ -- Useful info
https://cloud.google.com/learn/what-is-artificial-intelligence?hl=es -- AI introduction
https://github.com/cloudanum/50algorithms/tree/main -- Useful maths info
https://www.kaggle.com -- ML resources site
https://www.fast.ai -- Useful info
https://www.oreilly.com/library/view/50-algorithms-every/9781803247762/ -- Math book
https://www.deeplearning.ai/resources/ -- Useful info
https://github.com/KoboldAI/KoboldAI-Client -- An useful project
https://github.com/artidoro/qlora -- Another useful project
I also highly recommend learning to use https://github.com and https://www.tensorflow.org/?hl=es-419
And learn to research! There is free info in youtube and reddit!
Information and research is always changing and updating, even more so in a popular subject like AI, feel free to contribute to the post with more information or correcting mine if I have made a mistake.
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