My AI journey

In my journey of entering the realm of AI, ML (Machine Learning) and DL (Deep Learning) I’ve come across several useful resources that have boosted my learning on the subject. Below you’ll find a list of carefully selected sources that I myself have used to gain more knowledge about a particular domain related to AI.

Courses

• Machine Learning with Python-From Linear Models to Deep Learning 2023 (paid for certificate) - https://learning.edx.org/course/course-v1:MITx+6.86x+3T2023/home
• Course FastAI, Practical Deep Learning for Coders 2022 (free)- https://course.fast.ai/Resources/book.html
• Course FastAI, Github repository – https://github.com/fastai/fastbook
• Course Python Programming, Helsinki University (free)- https://programming-23.mooc.fi/
• Course Elements of AI, Helsinki University (free) - https://buildingai.elementsofai.com/
• Colab repository and configuration - https://course19.fast.ai/start_colab.html
• Dive into Deep Learning - https://d2l.ai/

Miscellaneous

• Python official documentation - https://d2l.ai/
• Numpy User Guide - https://numpy.org/doc/stable/index.html
• Tutorial on HuffingFace and Gradio - https://www.tanishq.ai/blog/gradio_hf_spaces_tutorial
• Introduction to LangChain - https://python.langchain.com/docs/get_started/introduction.html

AI Papers

• Paper on foundational models, 2022 - https://arxiv.org/pdf/2108.07258.pdf