Ask HN: Daily practices for building AI/ML skills?

• awesome-ai-papers

  5 502 8.7

  A curated list of the most impressive AI papers

In case you're unsure which papers would be good to implement, here's a nice GitHub repo: https://github.com/aimerou/awesome-ai-papers

Try out the "historical papers"! :)

• AntiPython-AI-Club

  2 28 10.0 C

  Discontinued AI for people who don't like Python [Moved to: https://github.com/Fileforma/AntiPython-AI-Compiler-Colab]

  

If you're interested in AI but dislike Python you can join the Anti Python AI club here: https://github.com/Fileforma/AntiPython-AI-Club

We work together to build AI models in our favorite programming languages.

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• minGPT

  34 18,789 0.0 Python

  A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training

minGPT (Karpathy): https://github.com/karpathy/minGPT

Next, some foundational textbooks for general ML and deep learning:

• course22p2

  6 430 2.0 Jupyter Notebook

  course.fast.ai 2022 part 2

  

Practical Deep Learning for Coders: https://course.fast.ai/Lessons/part2.html

• LLMsPracticalGuide

  11 8,525 4.5

  A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)

• deep_learning_curriculum

  1 996 5.4

  Language model alignment-focused deep learning curriculum

[3] - https://github.com/jacobhilton/deep_learning_curriculum

• mlops-course

  20 2,739 2.1 Jupyter Notebook

  Learn how to design, develop, deploy and iterate on production-grade ML applications.

coming from a similar context, i believe going top down might be the way to go.

up to your motivation, doing basic level courses first (as shared by others) and then tackling your own application of the concepts might be the way to go.

i also observe the need for strong IT skills for implementing end-to-end ml systems. so, you can play to your strenghts and also consider working on MLOps. (online self-paced course - https://github.com/GokuMohandas/mlops-course)

i went back to school to get structured learning. whether you find it directly useful or not, i found it more effective than just motivating myself to self-learn dry theory. down the line, if you want to go all-in, this might be a good option for you too.

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

• awesome-chatgpt-prompts

  157 103,383 7.0 HTML

  This repo includes ChatGPT prompt curation to use ChatGPT better.

I've found the following resources helpful:

- 15 Rules For Crafting Effective GPT Chat Prompts (https://expandi.io/blog/chat-gpt-rules/)

- Awesome ChatGPT Prompts (https://github.com/f/awesome-chatgpt-prompts)

For more resources of like nature, you can search for "mega prompt".

Suggest a related project