Keeping up

In the past, I’ve been asked for recommendations on how to keep up with the ever changing landscape of AI-ML, LLMs, Generative AI, etc. So, here are some resources I use stay updated no particular order:

• The Data Exchange podcast by Ben Lorica is excellent. The breadth and depth of his guests make it an irreplaceable source for keeping up with AI-ML research.

• Ahead of AI by Sebastian Raschka - writes on Substack on the latest in Natural Language Processing (NLP) research. I especially appreciate his ability to cut through the volume of published research and surface advancements.

• [MLOps newsletter]

Tools

Here are some tools from the AI-ML space that I’ve used in the past that I find useful and relevant.

Model Gardens

• Hugging Face - open model repository, host a mind-boggling variety of trained deep learning models across use cases.
• FastAPI
• Ray
• Streamlit
• SciKit-Learn

NLP specific

• LangChain
• SpaCy
• Gensim
• NLTK

AI-ML tutorials

Various tutorials I’ve bookmarked over the years:

• Keras.io tutorials - Keras and TensorFlow specific but a great resource for learning how to use the Keras API
• [Hugging Face]

(Note: I’m not affiliated with any of these products / organizations unless specified)