libraries

LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback. Link Link Link Link Link

This is the official repository of DeepMTP, a deep learning framework that can be used with multi-target prediction (MTP) problems. MTP can be seen as an umbrella term that cover many subareas of machine learning, which include multi-label classification (MLC), multivariate regression (MTR), multi-task learning (MTL), dyadic prediction (DP), and matrix completion (MC). The implementation is mainly written in Python and uses Pytorch for the implementation of the neural network. The goal is for any user to be able to train a model using only a few lines of code.

Hugging Face examples

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. Link

The facets project contains two visualizations for understanding and analyzing machine learning datasets: Facets Overview and Facets Dive. Link

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/}

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) 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. Link

Captum (“comprehension” in Latin) is an open source, extensible library for model interpretability built on PyTorch. Link

For data scientists and data engineers, d6tflow is a python library which makes building complex data science workflows easy, fast and intuitive. It is primarily designed for data scientists to build better models faster. For data engineers, it can also be a lightweight alternative and help productionize data science models faster. Unlike other data pipeline/workflow solutions, d6tflow focuses on managing data science research workflows instead of managing production data pipelines.

Spacetimeformer is a Transformer that learns temporal patterns like a time series model and spatial patterns like a Graph Neural Network. Link

Transfer learning refers to the process of pre-training a flexible model on a large dataset and using it later on other data with little to no training. It is one of the most outstanding 🚀 achievements in Machine Learning 🧠 and has many practical applications. Link

dstack is a lightweight and open-source command-line tool to provision infrastructure for ML workflows. Link

Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow Columnar Format as the memory model. Polars book Link

For data scientists and data engineers, d6tflow is a python library which makes building complex data science workflows easy, fast and intuitive. It is primarily designed for data scientists to build better models faster. For data engineers, it can also be a lightweight alternative and help productionize data science models faster. Unlike other data pipeline/workflow solutions, d6tflow focuses on managing data science research workflows instead of managing production data pipelines.