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Examples and guides for using the OpenAI API
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
Keyvi - the key value index. It is an in-memory FST-based data structure highly optimized for size and lookup performance.
🪄 Turns your machine learning code into microservices with web API, interactive GUI, and more.
ncnn is a high-performance neural network inference framework optimized for the mobile platform
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs o…
A unified, comprehensive and efficient recommendation library
Project for open sourcing research efforts on Backward Compatibility in Machine Learning
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Low-code framework for building custom LLMs, neural networks, and other AI models
Roadmap to becoming an Artificial Intelligence Expert in 2022
Deep-Learning based CTR models implemented by PyTorch
Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch…
Tensors and Dynamic neural networks in Python with strong GPU acceleration
🍼Debug Bottle is an Android runtime debug / develop tools written using kotlin language.
A PyTorch framework for an image retrieval task including implementation of N-pair Loss (NIPS 2016) and Angular Loss (ICCV 2017).
Incremental Skip-gram Model with Negative Sampling
CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc.
Lightweight, modular, and extensible library for functional programming.
Apache Superset is a Data Visualization and Data Exploration Platform
li-haoran / DRL-FlappyBird
Forked from floodsung/DRL-FlappyBirdPlaying Flappy Bird Using Deep Reinforcement Learning (Based on Deep Q Learning DQN using MXNet)