Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.

Template designed to kickstart your machine learning projects in Python

The official Python library for Openlayer, the Continuous Model Improvement Platform for AI. 📈

An Open Source Machine Learning Framework for Everyone

🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.

AI + Data, online. https://vespa.ai

This repository features a sentiment analysis model using LSTM networks, trained on IMDB reviews dataset. It includes a Jupyter notebook for training and evaluation, along with a pre-trained model file. Use this to analyze sentiment in text data with an LSTM-based approach.

Translating English to Hindi by training DL Models.

A simple, easy-to-understand library for diffusion models using Flax and Jax. Includes detailed notebooks on DDPM, DDIM, and EDM with simplified mathematical explanations. Made as part of my journey for learning and experimenting with state-of-the-art generative AI.

A retargetable MLIR-based machine learning compiler and runtime toolkit.

Modular Natural Language Processing workflows with Keras

To preform image classification on Rice Leaf Disease images using CNN via various methods.

An advanced platform for quantitative trading strategies, including AI-driven price prediction models and user management systems. Emulating institutional-grade practices like Citadel, it facilitates the development, training, and deployment of machine learning models for precise market forecasting.

A library for training and deploying machine learning models on Amazon SageMaker

Pwneu

Deep Learning for humans

A flexible, high-performance serving system for machine learning models

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

High-performance automatic differentiation of LLVM and MLIR.

This project is aimed at developing a model to recognize the state of solar panels and classify them using the VGG16 architecture as a base for feature extraction and is fine-tuned to achieve accurate classification results.