How agents execute human instructions with varying degrees of agency, from predefined steps to complete autonomy. Smolagents, a Huggingface library, which uses CodeAgent to enhance agent capabilities.
Tag: machine learning
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Reactions on Generative Models
Generative models like ChatGPT and Stable Diffusion have made waves around the world. Experts on machine learning and even different fields are weighing in on the implications of such powerful models. How will these change industries? I’ve talked about it in a previous blog, but now, more than ever, ethicists are required to bridge the […]
I’m going to use NVTabular with PyTorch Lightning to train a wide and deep recommender model on MovieLens 25M. It’s quite a chimera implementation as you shall see.
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NVIDIA Merlin on a Potato PC Setup
NVIDIA Merlin is a high-level open-source library to accelerate recommender systems on NVIDIA GPUs at scale. I’m on a potato PC setup relying on free instances. Can I make it work?
Header image note: I typed ‘variation’ in Pexels and it popped up. Masarap! We can turn a simple autoencoder into something sophisticated. Autoencoders discover a latent mapping z, which as a lower-dimensional representation of the input x, can be useful for pre-training networks and creating recommender models. However, how autoencoders compress information may come in […]
Medicine is quite different from my profession but I think having this kind of interest is healthy (get it?). Eventually, I came to reach some parallels between my discipline and the medical sciences.
Autoencoders are a simple neural network approach to recommendation
Variational autoencoders can discover unique ways to encode-decode the input from a distribution. Bonus: It can generate images!
Back in college, I did a class project where I used computer vision techniques for the first time. It was the age before deep learning. Today, I want to revisit this old project.
A tutorial to create a recommender pipeline with Kedro and MLFlow.
It is the REAL DYL 