fairseq-py - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

•    Python

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This is a PyTorch version of fairseq, a sequence-to-sequence learning toolkit from Facebook AI Research. The original authors of this reimplementation are (in no particular order) Sergey Edunov, Myle Ott, and Sam Gross. The toolkit implements the fully convolutional model described in Convolutional Sequence to Sequence Learning and features multi-GPU training on a single machine as well as fast beam search generation on both CPU and GPU. We provide pre-trained models for English to French and English to German translation. Currently fairseq-py requires PyTorch version >= 0.3.0. Please follow the instructions here: https://github.com/pytorch/pytorch#installation.

pytorch artificial-intelligence

translate - Translate - a PyTorch Language Library

•    Python

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Translate is a library for machine translation written in PyTorch. It provides training for sequence-to-sequence models. Translate relies on fairseq, a general sequence-to-sequence library, which means that models implemented in both Translate and Fairseq can be trained. Translate also provides the ability to export some models to Caffe2 graphs via ONNX and to load and run these models from C++ for production purposes. Currently, we export components (encoder, decoder) to Caffe2 separately and beam search is implemented in C++. In the near future, we will be able to export the beam search as well. We also plan to add export support to more models. Provided you have CUDA installed you should be good to go.

artificial-intelligence machine-learning onnx pytorch

pytorch-pretrained-BigGAN - 🦋A PyTorch implementation of BigGAN with pretrained weights and conversion scripts

•    Python

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An op-for-op PyTorch reimplementation of DeepMind's BigGAN model with the pre-trained weights from DeepMind. This repository contains an op-for-op PyTorch reimplementation of DeepMind's BigGAN that was released with the paper Large Scale GAN Training for High Fidelity Natural Image Synthesis by Andrew Brock, Jeff Donahue and Karen Simonyan.

computer-vision neural-network pytorch artificial-intelligence generative-adversarial-network gan biggan

pytorch-lightning - The lightweight PyTorch wrapper for high-performance AI research

•    Python

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The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate. Lightning disentangles PyTorch code to decouple the science from the engineering.

data-science machine-learning ai deep-learning pytorch artificial-intelligence

deep-learning-book - Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

•    Jupyter

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Repository for the book Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python. Deep learning is not just the talk of the town among tech folks. Deep learning allows us to tackle complex problems, training artificial neural networks to recognize complex patterns for image and speech recognition. In this book, we'll continue where we left off in Python Machine Learning and implement deep learning algorithms in PyTorch.

deep-learning neural-network machine-learning tensorflow artificial-intelligence data-science pytorch

SLM-Lab - Modular Deep Reinforcement Learning framework in PyTorch.

•    Python

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Modular Deep Reinforcement Learning framework in PyTorch. A multitask agent solving both OpenAI Cartpole-v0 and Unity Ball2D.

reinforcement-learning pytorch openai-gym framework research dqn artificial-intelligence policy-gradient actor-critic ppo a3c deep-rl

kaolin - A PyTorch Library for Accelerating 3D Deep Learning Research

•    Python

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NVIDIA Kaolin library provides a PyTorch API for working with a variety of 3D representations and includes a growing collection of GPU-optimized operations such as modular differentiable rendering, fast conversions between representations, data loading, 3D checkpoints and more. Kaolin library is part of a larger suite of tools for 3D deep learning research. For example, the Omniverse Kaolin App will allow interactive visualization of 3D checkpoints. To find out more about the Kaolin ecosystem, visit the NVIDIA Kaolin Dev Zone page.

model-zoo pytorch artificial-intelligence neural-networks 3d-deep-learning differentiable-rendering

XLearning - AI on Hadoop

•    Java

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XLearning is a convenient and efficient scheduling platform combined with the big data and artificial intelligence, support for a variety of machine learning, deep learning frameworks. XLearning is running on the Hadoop Yarn and has integrated deep learning frameworks such as TensorFlow, MXNet, Caffe, Theano, PyTorch, Keras, XGBoost. XLearning has the satisfactory scalability and compatibility.Besides the distributed mode of TensorFlow and MXNet frameworks, XLearning supports the standalone mode of all deep learning frameworks such as Caffe, Theano, PyTorch. Moreover, XLearning allows the custom versions and multi-version of frameworks flexibly.

hadoop tensorflow caffe mxnet yarn

stanfordnlp - Official Stanford NLP Python Library for Many Human Languages

•    Python

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The Stanford NLP Group's official Python NLP library. It contains packages for running our latest fully neural pipeline from the CoNLL 2018 Shared Task and for accessing the Java Stanford CoreNLP server. For detailed information please visit our official website. The PyTorch implementation of the neural pipeline in this repository is due to Peng Qi and Yuhao Zhang, with help from Tim Dozat and Jason Bolton.

nlp natural-language-processing machine-learning deep-learning artificial-intelligence pytorch universal-dependencies

pytorch-dense-correspondence - Code for "Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation"

•    Python

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Abstract: What is the right object representation for manipulation? We would like robots to visually perceive scenes and learn an understanding of the objects in them that (i) is task-agnostic and can be used as a building block for a variety of manipulation tasks, (ii) is generally applicable to both rigid and non-rigid objects, (iii) takes advantage of the strong priors provided by 3D vision, and (iv) is entirely learned from self-supervision. This is hard to achieve with previous methods: much recent work in grasping does not extend to grasping specific objects or other tasks, whereas task-specific learning may require many trials to generalize well across object configurations or other tasks. In this paper we present Dense Object Nets, which build on recent developments in self-supervised dense descriptor learning, as a consistent object representation for visual understanding and manipulation. We demonstrate they can be trained quickly (approximately 20 minutes) for a wide variety of previously unseen and potentially non-rigid objects. We additionally present novel contributions to enable multi-object descriptor learning, and show that by modifying our training procedure, we can either acquire descriptors which generalize across classes of objects, or descriptors that are distinct for each object instance. Finally, we demonstrate the novel application of learned dense descriptors to robotic manipulation. We demonstrate grasping of specific points on an object across potentially deformed object configurations, and demonstrate using class general descriptors to transfer specific grasps across objects in a class. To prevent the repo from growing in size, recommend always "restart and clear outputs" before committing any Jupyter notebooks. If you'd like to save what your notebook looks like, you can always "download as .html", which is a great way to snapshot the state of that notebook and share.

computer-vision deep-learning robotics pytorch artificial-intelligence vision manipulation 3d self-supervised-learning

fairseq - Facebook AI Research Sequence-to-Sequence Toolkit

•    Lua

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This is fairseq, a sequence-to-sequence learning toolkit for Torch from Facebook AI Research tailored to Neural Machine Translation (NMT). It implements the convolutional NMT models proposed in Convolutional Sequence to Sequence Learning and A Convolutional Encoder Model for Neural Machine Translation as well as a standard LSTM-based model. It features multi-GPU training on a single machine as well as fast beam search generation on both CPU and GPU. We provide pre-trained models for English to French, English to German and English to Romanian translation. Note, there is now a PyTorch version fairseq-py of this toolkit and new development efforts will focus on it.

madgrad - MADGRAD Optimization Method

•    Python

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Try it out! A best-of-both-worlds optimizer with the generalization performance of SGD and at least as fast convergence as that of Adam, often faster. A drop-in torch.optim implementation madgrad.MADGRAD is provided, as well as a FairSeq wrapped instance. For FairSeq, just import madgrad anywhere in your project files and use the --optimizer madgrad command line option, together with --weight-decay, --momentum, and optionally --madgrad_eps. The madgrad.py file containing the optimizer can be directly dropped into any PyTorch project if you don't want to install via pip. If you are using fairseq, you need the acompanying fairseq_madgrad.py file as well.

Machine-Learning / Deep-Learning / AI + Web3 -Tutorials

•    Python

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A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

machine-learning deep-learning tensorflow pytorch keras matplotlib aws kaggle pandas scikit-learn torch artificial-intelligence neural-network convolutional-neural-networks tensorflow-tutorials python-data ipython-notebook capsule-network

polyaxon - An open source platform for reproducible machine learning and deep learning on kubernetes

•    Python

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Welcome to Polyaxon, a platform for building, training, and monitoring large scale deep learning applications. Polyaxon deploys into any data center, cloud provider, or can be hosted and managed by Polyaxon, and it supports all the major deep learning frameworks such as Tensorflow, MXNet, Caffe, Torch, etc.

deep-learning machine-learning artificial-intelligence data-science reinforcement-learning kubernetes tensorflow pytorch keras mxnet caffe ai dl ml k8s

NeuronBlocks - NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego

•    Python

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NeuronBlocks is a NLP deep learning modeling toolkit that helps engineers/researchers to build end-to-end pipelines for neural network model training for NLP tasks. The main goal of this toolkit is to minimize developing cost for NLP deep neural network model building, including both training and inference stages. NeuronBlocks consists of two major components: Block Zoo and Model Zoo.

question-answering deep-learning pytorch natural-language-processing text-classification artificial-intelligence dnn qna text-matching knowledge-distillation model-compression

RedisAI - A Redis module for serving tensors and executing deep learning graphs

•    Java

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RedisAI is a Redis module for executing Deep Learning/Machine Learning models and managing their data. Its purpose is being a "workhorse" for model serving, by providing out-of-the-box support for popular DL/ML frameworks and unparalleled performance. RedisAI both maximizes computation throughput and reduces latency by adhering to the principle of data locality , as well as simplifies the deployment and serving of graphs by leveraging on Redis' production-proven infrastructure.

pytorch tensorflow onnxruntime serving-tensors machine-learning deep-learning artificial-intelligence

RUBRIX - Python framework to explore, label, and monitor data for NLP

•    Python

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Rubrix is a production-ready Python framework for exploring, annotating, and managing data in NLP projects. Most annotation tools treat data collection as a one-off activity at the beginning of each project. In real-world projects, data collection is a key activity of the iterative process of ML model development. Once a model goes into production, you want to monitor and analyze its predictions, and collect more data to improve your model over time. Rubrix is designed to close this gap, enabling you to iterate as much as you need.

nlp elasticsearch data-science machine-learning natural-language-processing pytorch artificial-intelligence weak-supervision knowledge-graph developer-tools active-learning annotation-tool weakly-supervised-learning human-in-the-loop mlops text-labeling

Stanza - A Python NLP Library for Many Human Languages

•    Python

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Stanza is a Python NLP Library for Many Human Languages. It contains support for running various accurate natural language processing tools on 60+ languages and for accessing the Java Stanford CoreNLP software from Python. A new collection of biomedical and clinical English model packages are now available, offering seamless experience for syntactic analysis and named entity recognition (NER) from biomedical literature text and clinical notes.

nlp machine-learning natural-language-processing deep-learning pytorch artificial-intelligence named-entity-recognition universal-dependencies corenlp

MindsDB - In-Database Machine Learning

•    Python

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MindsDB enables you to use ML predictions in your database using SQL. MindsDB automates and abstracts machine learning models through virtual AI Tables. It can easily make predictions over very complex multivariate time-series data with high cardinality.

machine-learning clickhouse postgresql ml snowflake mariadb pytorch artificial-intelligence machine-learning-api hacktoberfest automl explainable-ai explainable-ml singlestore

pytorch-tutorial - PyTorch Tutorial for Deep Learning Researchers

•    Python

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This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.

deep-learning pytorch-tutorial neural-networks pytorch tutorial tensorboard