个人整理的人工智能资源
- Awesome Federated Learning
https://github.com/poga/awesome-federated-learning
A list of resources releated to federated learning and privacy in machine learning.
Open Neural Network Exchange (ONNX) is the first step toward an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Initially we focus on the capabilities needed for inferencing (evaluation).
Caffe2, PyTorch, Microsoft Cognitive Toolkit, Apache MXNet and other tools are developing ONNX support. Enabling interoperability between different frameworks and streamlining the path from research to production will increase the speed of innovation in the AI community. We are an early stage and we invite the community to submit feedback and help us further evolve ONNX.
https://github.com/apache/incubator-mxnet
Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.
MXNet is also more than a deep learning project. It is also a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.
https://github.com/awslabs/mxnet-model-server
Apache MXNet Model Server (MMS) is a flexible and easy to use tool for serving deep learning models exported from MXNet or the Open Neural Network Exchange (ONNX).
Use the MMS Server CLI, or the pre-configured Docker images, to start a service that sets up HTTP endpoints to handle model inference requests.
https://github.com/tensorflow/lucid
Lucid is a collection of infrastructure and tools for research in neural network interpretability.
https://github.com/deepmind/sonnet
Sonnet is a library built on top of TensorFlow for building complex neural networks.
https://github.com/deepmind/dm_control
This package contains:
- A set of Python Reinforcement Learning environments powered by the MuJoCo physics engine. See the suite subdirectory.
- Libraries that provide Python bindings to the MuJoCo physics engine.
https://github.com/deepmind/graph_nets
Graph Nets is DeepMind's library for building graph networks in Tensorflow and Sonnet.
https://github.com/Microsoft/CNTK
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
https://github.com/Azure/mmlspark
MMLSpark provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.
https://github.com/Microsoft/MMdnn
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch and CoreML.
https://github.com/intel-analytics/BigDL
BigDL: Distributed Deep Learning on Apache Spark
https://github.com/blei-lab/edward
Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields: Bayesian statistics and machine learning, deep learning, and probabilistic programming.
https://github.com/RubensZimbres/Repo-2017
Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano
https://github.com/Featuretools/featuretools
Featuretools is a python library for automated feature engineering.
Keras 是一个用 Python 编写的高级神经网络 API，它能够以 TensorFlow, CNTK, 或者 Theano 作为后端运行。Keras 的开发重点是支持快速的实验。能够以最小的时延把你的想法转换为实验结果，是做好研究的关键。
如果你有如下需求，请选择 Keras：
- 允许简单而快速的原型设计（用户友好，高度模块化，可扩展性）。
- 同时支持卷积神经网络和循环神经网络，以及两者的组合。
- 在CPU和GPU上无缝运行与切换。
https://github.com/icoxfog417/awesome-financial-nlp
Researches for Natural Language Processing for Financial Domain.
https://github.com/sebastianruder/NLP-progress
This document aims to track the progress in Natural Language Processing (NLP) and give an overview of the state-of-the-art across the most common NLP tasks and their corresponding datasets.
https://github.com/dpressel/baseline
Simple, Strong Deep-Learning Baselines for NLP in several frameworks
Baseline algorithms and data support implemented with multiple deep learning tools, including sentence classification, tagging, seq2seq, and language modeling. Can be used as stand-alone command line tools or as a Python library. The library attempts to provide a common interface for several common deep learning tasks, as well as easy-to-use file loaders to make it easy to publish standard results, compare against strong baselines without concern for mistakes and to support rapid experiments to try and beat these baselines.
NLTK -- the Natural Language Toolkit -- is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing.
https://github.com/facebookresearch/ParlAI
ParlAI (pronounced “par-lay”) is a framework for dialog AI research, implemented in Python.
Its goal is to provide researchers:
- a unified framework for sharing, training and testing dialog models
- many popular datasets available all in one place, with the ability to multi-task over them
- seamless integration of Amazon Mechanical Turk for data collection and human evaluation
https://github.com/allenai/deep_qa
DeepQA is a library for doing high-level NLP tasks with deep learning, particularly focused on various kinds of question answering. DeepQA is built on top of Keras and TensorFlow, and can be thought of as an interface to these systems that makes NLP easier.
https://github.com/siddk/deep-nlp
This repository contains Tensorflow implementations of various deep learning models, with a focus on problems in Natural Language Processing. Each individual subdirectory is self-contained, addressing one specific model.
we provide details of a newly created dataset of Chinese text with about 1 million Chinese characters annotated by experts in over 30 thousand street view images. This is a challenging dataset with good diversity. It contains planar text, raised text, text in cities, text in rural areas, text under poor illumination, distant text, partially occluded text, etc. For each character in the dataset, the annotation includes its underlying character, its bounding box, and 6 attributes. The attributes indicate whether it has complex background, whether it is raised, whether it is handwritten or printed, etc.
https://github.com/tramphero/kaldi
This is now the official location of the Kaldi project. http://kaldi-asr.org
https://github.com/NervanaSystems/nlp-architect
NLP Architect by Intel AI Lab: Python library for exploring the state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding http://nlp_architect.nervanasys.com/
https://github.com/thunlp/OpenNRE
An open-source framework for neural relation extraction.
https://github.com/google-research/bert
BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks.
Our academic paper which describes BERT in detail and provides full results on a number of tasks can be found here: https://arxiv.org/abs/1810.04805.
https://github.com/oxford-cs-deepnlp-2017/lectures
This repository contains the lecture slides and course description for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford.
https://github.com/joanbruna/stat212b
Topics Course on Deep Learning for Spring 2016, by Joan Bruna, UC Berkeley, Statistics Department
https://github.com/sjchoi86/Deep-Learning-101
Deep Learning Tutorials These tutorials are for deep learning beginners which have been used in a six week Deep Learning and Computer Vision course. Hope these to be helpful for understanding what deep learning is and how it can be applied to various fields including computer vision, robotics, natural language processings, and so forth.
https://github.com/krasserm/machine-learning-notebooks
Stanford Machine Learning course exercises implemented with scikit-learn
https://katefvision.github.io/
- Implement and experiment with existing algorithms for learning control policies guided by reinforcement, expert demonstrations or self-trials.
- Evaluate the sample complexity, generalization and generality of these algorithms.
- Be able to understand research papers in the field of robotic learning.
- Try out some ideas/extensions of your own. Particular focus on incorporating true sensory signal from vision or tactile sensing, and exploring the synergy between learning from simulation versus learning from real experience.
This class takes an engineering approach to exploring possible research paths toward building human-level intelligence. The lectures will introduce our current understanding of computational intelligence and ways in which strong AI could possibly be achieved, with insights from deep learning, reinforcement learning, computational neuroscience, robotics, cognitive modeling, psychology, and more. Additional topics will include AI safety and ethics. Projects will seek to build intuition about the limitations of state-of-the-art machine learning approaches and how those limitations may be overcome. The course will include several guest talks. Listeners are welcome.
https://nancysbraintalks.mit.edu/course/9-11-the-human-brain
MIT认知神经科学教授Nancy Kanwisher，放出了一大波本学期（2018年春季）MIT本科生课程人类大脑（The Human Brain）的视频，课程代号MIT 9.11。
http://bair.berkeley.edu/courses.html
http://rll.berkeley.edu/deeprlcourse/#syllabus
https://github.com/sjchoi86/Tensorflow-101
Tensorflow Tutorials using Jupyter Notebook
TensorFlow tutorials written in Python (of course) with Jupyter Notebook. Tried to explain as kindly as possible, as these tutorials are intended for TensorFlow beginners. Hope these tutorials to be a useful recipe book for your deep learning projects. Enjoy coding! :)
https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap
If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?"
Here is a reading roadmap of Deep Learning papers!
https://github.com/terryum/awesome-deep-learning-papers
Awesome - Most Cited Deep Learning Papers
A curated list of the most cited deep learning papers (since 2012)
We believe that there exist classic deep learning papers which are worth reading regardless of their application domain. Rather than providing overwhelming amount of papers, We would like to provide a curated list of the awesome deep learning papers which are considered as must-reads in certain research domains.
https://developers.google.cn/machine-learning/crash-course/
课程的主要架构分为三个部分：机器学习概念（18讲）、机器学习工程（4讲）、机器学习在现实世界的应用示例（3讲）。
https://github.com/jacobeisenstein/gt-nlp-class/tree/master/notes
These notes are the basis for the readings in CS4650 and CS7650 ("Natural Language") at Georgia Tech.
乔治亚理工大学 Jacob Eisenstein 教授开放了自然语言处理领域的最新教材《Natural Language Processing》
https://github.com/yandexdataschool/nlp_course
YSDA course in Natural Language Processing
https://github.com/hibayesian/awesome-automl-papers
A curated list of automated machine learning papers, articles, tutorials, slides and projects.
https://github.com/markdtw/awesome-architecture-search
https://github.com/automl/auto-sklearn
https://github.com/dotnet/machinelearning
主要学习框架
https://github.com/mlperf/reference
Reference implementations of MLPerf benchmarks
https://github.com/salesforce/TransmogrifAI
TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library written in Scala that runs on top of Spark. It was developed with a focus on accelerating machine learning developer productivity through machine learning automation, and an API that enforces compile-time type-safety, modularity, and reuse. Through automation, it achieves accuracies close to hand-tuned models with almost 100x reduction in time.
https://github.com/Microsoft/nni
NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning experiments. The tool dispatches and runs trial jobs that generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments (e.g. local machine, remote servers and cloud).
https://github.com/tensorflow/adanet
AdaNet is a lightweight and scalable TensorFlow AutoML framework for training and deploying adaptive neural networks using the AdaNet algorithm [Cortes et al. ICML 2017]. AdaNet combines several learned subnetworks in order to mitigate the complexity inherent in designing effective neural networks.
http://acemap.sjtu.edu.cn/app/AceKG/
AceKG describes 114.30 million academic entities based on a consistent ontology, including 61,704,089 papers, 52,498,428 authors, 50,233 research fields, 19,843 academic institutes, 22,744 journals, 1,278 conferences and 3 special affiliations. In total, AceKG consists of 2.2 billion pieces of relationship information.
UC Berkeley 发布了迄今为止规模最大、最多样化的开放驾驶视频数据集——BDD100K。该数据集共包含 10 万个视频，BAIR 研究者在视频上采样关键帧，并为这些关键帧提供注释。此外，BAIR 还将在 CVPR 2018 自动驾驶 Workshop 上基于其数据举办三项挑战赛。
https://datacatalog.worldbank.org/
https://ai.tencent.com/ailab/nlp/embedding.html
This corpus provides 200-dimension vector representations, a.k.a. embeddings, for over 8 million Chinese words and phrases, which are pre-trained on large-scale high-quality data. These vectors, capturing semantic meanings for Chinese words and phrases, can be widely applied in many downstream Chinese processing tasks (e.g., named entity recognition and text classification) and in further research.