Graph ML in 2023: The State of Affairs
2022 comes to an end and it is about time to sit down and reflect upon the achievements made in Graph ML as well as to hypothesize about possible breakthroughs in 2023.
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Machine Learning
Machine learning is a field of artificial intelligence (AI) that is concerned with learning from data. Machine learning has three components:
- Supervised learning: Fitting predictive models using data for which outcomes are available.
- Unsupervised learning: Transforming and partitioning data where outcomes are not available.
- Reinforcement learning: on-line learning in environments where not all events are observable. Reinforcement learning is frequently applied in robotics.
Posts on machine learning
In the following posts, machine learning is applied to solve problems using R.
Welcome to LightFM’s documentation!
LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback.
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Notebooks using the Hugging Face libraries
Hugging Face examples
Recommending Amazon Products using Graph Neural Networks in PyTorch Geometric
In this report, we will walk you through how you can leverage PyTorch Geometric along with Weights & Biases to analyse the Amazon products graph and recommend products from the graph.
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ChatGPT Telegram Bot
This is a Telegram bot that lets you chat with the chatGPT language model using your local browser. The bot uses Playwright to run chatGPT in Chromium, and can parse code and text, as well as send messages. It also includes a /draw command that allows you to generate pictures using stable diffusion. More features are coming soon.
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DeepNash learns to play Stratego from scratch by combining game theory and model-free deep RL
Game-playing artificial intelligence (AI) systems have advanced to a new frontier. Stratego, the classic board game that’s more complex than chess and Go, and craftier than poker, has now been mastered. Published in Science, we present DeepNash, an AI agent that learned the game from scratch to a human expert level by playing against itself.
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Efficient and scalable machine learning pipelines
A well-established rule of thumb that applies to most machine learning projects is that the larger and cleaner the dataset, the better the performance.
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GEOTORCH A Deep Learning and Scalable Data Processing Framework for Raster and Spatio-Temporal Datasets
GeoTorch is a python library on top of PyTorch and Apache Sedona for deep learning and scalable data processing focusing on raster imagery and spatio-temporal non-imagery datasets. It has various modules for deep learning and data preprocessing under both categories of datasets. The deep learning module offers ready-to-use datasets, models, and
[Link]{https://kanchanchy.github.io/geotorch/}
Hugging Face Diffusion Models Course
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I Used ChatGPT to Create an Entire AI Application on AWS
This new language model could be the pair programmer of your choice going forward
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OpenMetricLearning
This post is related to the recent release of a new open-source project called OpenMetricLearning (OML), and one of its goals is to lower the entry threshold for metric learning pipelines. We will briefly introduce the theory, discuss the examples in code and show how simple heuristics can perform on a level comparable with the current SotA. Since the project is new, each star on GitHub is essential for us.
Ray AI Runtime (AIR)
Ray AI Runtime (AIR) is an open-source toolkit for building ML applications. It provides libraries for distributed data processing, model training, tuning, reinforcement learning, model serving, and more.
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This Not That (TNT)
This Not That is a suite of tools for working with, exploring, and interacting with “data maps”. A data map is a two (or three) dimensional representation of higher dimensional vector space data, usually produced by UMAP, t_SNE, or another manifold learning technique.
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An easy-to-use & supercharged open-source experiment tracker
Aim logs your training runs, enables a beautiful UI to compare them and an API to query them programmatically.
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Awesome Production Machine Learning
This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale and secure your production machine learning
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