Have you tried https://geobase.app/ they recently also had a post about duckdb integration: https://geobase.app/blog/duckdb-1-1-3

I had not, looks pretty cool but solves the inverse of the problem as I see it. I want a backend agnostic frontend toolset that is a GIS that I can customize to my needs. I don't want to implement the tools myself, that's too low level. I don't want the service to manage, control, or own the data, that's too high level. There's a sweet spot I don't think is being hit yet.

A novel and memory-friendly preference alignment method for diffusion models that does not depend on any reference model.

The dataset can be used for training preference models or aligning language models through techniques like Direct Preference Optimization (DPO).

An efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance!

The title is confusing but refers to the Huggingface Diffusers v0.15 release which brings new pipelines for video and audio to diffusers, showing that diffusion is a great choice for all sorts of generative tasks.

All the steps involved in training a LlaMa model to answer questions on Stack Exchange data with RLHF.

You could of course use your own question and answer data to refine the model using the same process. I wonder if anyone has tried that yet to, for instance, fine tune LlaMa to answer support queries for their company?

Diffusers now supports CompVis, Stability AI and LAION's Stable Diffusion model weights.

A library of pretrained diffusion models across multiple modalities, such as vision and audio.

With all the terminal recordings I have seen, the content eventually ends up at the bottom. It would be awesome to have the cursor always stay in the middle... any idea if thats possible with Asciinema?

Why do you want to have it in the middle? Then you waste half the screen space right?

If you want it to push from the top, I could understand... but in the middle?

Form my own journey I would say that a good place to start for graphical models might be "Bayesian Reasoning and Machine Learning" by Barber. It's free (http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=...). I haven't read through it, but I've heard good things. However, it doesn't cover some basic things like SVM, RVM, Neural Networks...

For those I'd suggest "Pattern Recognition and Machine Learning" by Bishop. I've read throughout this and it's really well organized and thought out. For more mathematically advanced ML stuff I'd suggest "Foundations of Machine Learning" by Mohri. For a good reference for anything else I'd suggest "Machine Learning: A Probabilistic Perspective" by Murphy. For more depth on graphical models look at "Probabilistic Graphical Models: Principles and Techniques" by Koller.

On the NLP front there's the standard texts "Speech and Language Processing" by Jurafsky and "Foundations of Statistical Natural Language Processing" by Manning.

I also like "An Introduction to Statistical Learning" by James, Witten, Hastie and Tibshirani.

I skimmed over Mohri's book and I think the topics it covers are quite narrow.

For mathematical foundations of ML, I would recommend the book "Understanding Machine Learning: From Theory to Algorithms" by Shai Shalev-Shwartz.

A brief version of the book is available to download on the author's website: http://www.cs.huji.ac.il/~shais/Handouts.pdf

Yes, Mohri's book takes a strong learning theory approach.

At the same time, it's the only book I've seen that covers online learning well. Can you think of any others?