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https://github.com/NEUChatbot/awesome-neuchatbot

Series of documents for chatbot development.
https://github.com/NEUChatbot/awesome-neuchatbot

List: awesome-neuchatbot

Last synced: 16 days ago
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Series of documents for chatbot development.

Awesome Lists containing this project

README 

        
# Awesome NEU Chatbot [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/NEUChatbot/awesome-neuchatbot)



## Table of Contents



* **[Free Online Books](#free-online-books)**  



* **[Courses](#courses)**  



* **[Videos and Lectures](#videos-and-lectures)**  



* **[Papers](#papers)**  



* **[Tutorials](#tutorials)**  



* **[WebSites](#websites)**  



* **[Datasets](#datasets)**



* **[Conferences](#Conferences)**



* **[Frameworks](#frameworks)**  



* **[Miscellaneous](#miscellaneous)**  



* **[Contributing](#contributing)**  



 

### Free Online Books



1.  [Deep Learning](http://www.deeplearningbook.org/) by Yoshua Bengio, Ian Goodfellow and Aaron Courville  (05/07/2015)

2.  [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) by  Michael Nielsen (Dec 2014)

3.  [Deep Learning](http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf) by Microsoft Research (2013) 

4.  [Deep Learning Tutorial](http://deeplearning.net/tutorial/deeplearning.pdf) by LISA lab, University of Montreal (Jan 6 2015)

5.  [neuraltalk](https://github.com/karpathy/neuraltalk) by Andrej Karpathy : numpy-based RNN/LSTM implementation

6.  [Deep Learning in Neural Networks: An Overview](http://arxiv.org/pdf/1404.7828v4.pdf)

 

### Courses



1. [Tensorflow for Deep Learning Research](https://web.stanford.edu/class/cs20si/) by Chip Huyen

2.  [Machine Learning - Stanford](https://class.coursera.org/ml-005) by Andrew Ng in Coursera (2010-2014)

3.  [Machine Learning - Carnegie Mellon](http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml) by Tom Mitchell (Spring 2011)



### Videos and Lectures



1. [Deep Learning, Self-Taught Learning and Unsupervised Feature Learning](https://www.youtube.com/watch?v=n1ViNeWhC24) By Andrew Ng

2. [Machine Learning](https://www.youtube.com/watch?v=CXgbekl66jc&list=PLJV_el3uVTsPy9oCRY30oBPNLCo89yu49) By Hung-yi Lee in NTU



### Papers



1.  [Using Very Deep Autoencoders for Content Based Image Retrieval](http://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf)

2.  [Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf)

3.  [CMU’s list of papers](http://deeplearning.cs.cmu.edu/)

4.  [Training Recurrent Neural Networks](http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf)

5.  [LSTM](http://web.eecs.utk.edu/~itamar/courses/ECE-692/Bobby_paper1.pdf)

6.  [GRU - Gated Recurrent Unit](http://arxiv.org/pdf/1406.1078v3.pdf)

7.  [GFRNN](http://arxiv.org/pdf/1502.02367v3.pdf) [.](http://jmlr.org/proceedings/papers/v37/chung15.pdf) [.](http://jmlr.org/proceedings/papers/v37/chung15-supp.pdf)

8.  [LSTM: A Search Space Odyssey](http://arxiv.org/pdf/1503.04069v1.pdf)

9.  [A Critical Review of Recurrent Neural Networks for Sequence Learning](http://arxiv.org/pdf/1506.00019v1.pdf)

10.  [Visualizing and Understanding Recurrent Networks](http://arxiv.org/pdf/1506.02078v1.pdf)

11.  [Recurrent Neural Network based Language Model](http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf)

12.  [Deep Neural Networks for Acoustic Modeling in Speech Recognition](http://cs224d.stanford.edu/papers/maas_paper.pdf)

13.  [Speech Recognition with Deep Recurrent Neural Networks](http://www.cs.toronto.edu/~fritz/absps/RNN13.pdf)

14.  [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](http://arxiv.org/pdf/1406.1078v3.pdf)

15. [Google - Sequence to Sequence  Learning with Neural Networks](http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf)

16. [Ask Me Anything: Dynamic Memory Networks for Natural Language Processing](http://arxiv.org/pdf/1506.07285v1.pdf)

17. [Batch Normalization](https://arxiv.org/abs/1502.03167)

18. [Berkeley AI Research (BAIR) Laboratory](https://arxiv.org/pdf/1611.07004v1.pdf) 

19. [On Using Very Large Target Vocabulary for Neural Machine Translation](https://arxiv.org/pdf/1412.2007.pdf)

20. [Attention Is All You Need](https://arxiv.org/pdf/1706.03762.pdf)

### Tutorials



1.  [A Deep Learning Tutorial: From Perceptrons to Deep Networks](http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks)

2.  [Deep Learning from the Bottom up](http://www.metacademy.org/roadmaps/rgrosse/deep_learning)

3.  [Theano Tutorial](http://deeplearning.net/tutorial/deeplearning.pdf)

4.  [Torch7 Tutorials](https://github.com/clementfarabet/ipam-tutorials/tree/master/th_tutorials)

5. [TensorFlow tutorials](https://github.com/nlintz/TensorFlow-Tutorials)

6. [More TensorFlow tutorials](https://github.com/pkmital/tensorflow_tutorials)

7. [TensorFlow Python Notebooks](https://github.com/aymericdamien/TensorFlow-Examples)

8. [Keras and Lasagne Deep Learning Tutorials](https://github.com/Vict0rSch/deep_learning)

9. [TensorFlow-World](https://github.com/astorfi/TensorFlow-World)

10. [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python)

11. [Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder](https://blog.sicara.com/keras-tutorial-content-based-image-retrieval-convolutional-denoising-autoencoder-dc91450cc511)

12. [Pytorch Tutorial by Yunjey Choi](https://github.com/yunjey/pytorch-tutorial)



### WebSites



1.  [deeplearning.net](http://deeplearning.net/)

2.  [deeplearning.stanford.edu](http://deeplearning.stanford.edu/)

3.  [cs.brown.edu/research/ai](http://cs.brown.edu/research/ai/)

4.  [eecs.umich.edu/ai](http://www.eecs.umich.edu/ai/)

5.  [cs.utexas.edu/users/ai-lab](http://www.cs.utexas.edu/users/ai-lab/)

6.  [cs.washington.edu/research/ai](http://www.cs.washington.edu/research/ai/)

7.  [aiai.ed.ac.uk](http://www.aiai.ed.ac.uk/)

8.  [csail.mit.edu](http://www.csail.mit.edu/)

9.  [cgi.cse.unsw.edu.au/~aishare](http://cgi.cse.unsw.edu.au/~aishare/)

10.  [cs.rochester.edu/research/ai](http://www.cs.rochester.edu/research/ai/)

11.  [ai.sri.com](http://www.ai.sri.com/)

12.  [isi.edu/AI/isd.htm](http://www.isi.edu/AI/isd.htm)

13.  [hips.seas.harvard.edu](http://hips.seas.harvard.edu/)

14.  [AI Weekly](http://aiweekly.co)

15.  [stat.ucla.edu](http://www.stat.ucla.edu/~junhua.mao/m-RNN.html)

16.  [deeplearning.cs.toronto.edu](http://deeplearning.cs.toronto.edu/i2t)

17.  [visualqa.org](http://www.visualqa.org/)

18.  [Deep Learning News](http://news.startup.ml/)

19.  [Machine Learning is Fun! Adam Geitgey's Blog](https://medium.com/@ageitgey/)

20.  [Guide to Machine Learning](http://yerevann.com/a-guide-to-deep-learning/)

21.  [Deep Learning for Beginners](https://spandan-madan.github.io/DeepLearningProject/)



### Datasets

1. [VQA](http://www.visualqa.org/) VQA is a new dataset containing open-ended questions about images. 

2. [Image QA](http://www.cs.toronto.edu/~mren/imageqa/data/cocoqa/)

3. [Columbia-Utrecht Reflectance and Texture Database](http://www.cs.columbia.edu/CAVE/curet/) - Texture and reflectance measurements for over 60 samples of 3D texture, observed with over 200 different combinations of viewing and illumination directions. (Formats: bmp)

4. [DeepMind QA Corpus](https://github.com/deepmind/rc-data) - Textual QA corpus from CNN and DailyMail. More than 300K documents in total. [Paper](http://arxiv.org/abs/1506.03340) for reference.

5. [Cornell Movie Dialogs](http://www.cs.cornell.edu/~cristian/Cornell_Movie-Dialogs_Corpus.html) - Movie dialogs.

6. [Ubuntu Dialogue Corpus](https://arxiv.org/abs/1506.08909) - Dialogs of ubuntu journal.

7. [OpenSubtitles](http://opus.lingfil.uu.se/OpenSubtitles.php) - Movie subtitles.

8. [Twitter](https://github.com/Marsan-Ma/twitter_scraper) - Dataset from twitter.

9. [Papaya Conversational Data Set](https://github.com/bshao001/ChatLearner) - Reorganized based on Cornell and Reddit.

10. [dgk_shooter_min.conv](https://github.com/rustch3n/dgk_lost_conv) - Chinese movie dialogs.

11. [白鹭时代中文问答语料](https://github.com/Samurais/egret-wenda-corpus) - 白鹭时代论坛问答数据，一个问题对应一个最好的答案。

12. [微博数据集](http://61.93.89.94/Noah_NRM_Data/) - 华为李航实验室发布，也是论文“Neural Responding Machine for Short-Text Conversation”使用的数据集。

13. [微博数据集](http://lwc.daanvanesch.nl/openaccess.php) - 新浪微博数据集，评论回复短句。

 

### Conferences



1. [IJCAI - 	International Joint Conference on Artificial Intelligence](https://www.ijcai-18.org/)

2. [ICML - 	International Conference on Machine Learning](https://icml.cc)

3. [ECML - European Conference on Machine Learning](http://www.ecmlpkdd2018.org)

4. [KDD - Knowledge Discovery and Data Mining](http://www.kdd.org/kdd2018/)

5. [NIPS - Neural Information Processing Systems](https://nips.cc/Conferences/2018)

6. [O'Reilly AI Conference - 	O'Reilly Artificial Intelligence Conference](https://conferences.oreilly.com/artificial-intelligence/ai-ny)

7. [AAAI - Association for the Advancement of Artificial Intelligence](https://www.aaai.org)



### Frameworks



1.  [Caffe](http://caffe.berkeleyvision.org/)  

2.  [Torch7](http://torch.ch/)

3.  [Theano](http://deeplearning.net/software/theano/)

4.  [cuda-convnet](https://code.google.com/p/cuda-convnet2/)

5.  [convetjs](https://github.com/karpathy/convnetjs)

5.  [Ccv](http://libccv.org/doc/doc-convnet/)

6.  [NuPIC](http://numenta.org/nupic.html)

7.  [DeepLearning4J](http://deeplearning4j.org/)

8.  [Brain](https://github.com/harthur/brain)

9.  [DeepLearnToolbox](https://github.com/rasmusbergpalm/DeepLearnToolbox)

10.  [Deepnet](https://github.com/nitishsrivastava/deepnet)

11.  [Deeppy](https://github.com/andersbll/deeppy)

12.  [JavaNN](https://github.com/ivan-vasilev/neuralnetworks)

13.  [hebel](https://github.com/hannes-brt/hebel)

14.  [Mocha.jl](https://github.com/pluskid/Mocha.jl)

15.  [OpenDL](https://github.com/guoding83128/OpenDL)

16.  [cuDNN](https://developer.nvidia.com/cuDNN)

17.  [MGL](http://melisgl.github.io/mgl-pax-world/mgl-manual.html)

18.  [Knet.jl](https://github.com/denizyuret/Knet.jl)

19.  [Nvidia DIGITS - a web app based on Caffe](https://github.com/NVIDIA/DIGITS)

20.  [Neon - Python based Deep Learning Framework](https://github.com/NervanaSystems/neon)

21.  [Keras - Theano based Deep Learning Library](http://keras.io)

22.  [Chainer - A flexible framework of neural networks for deep learning](http://chainer.org/)

23.  [RNNLM Toolkit](http://rnnlm.org/)

24.  [RNNLIB - A recurrent neural network library](http://sourceforge.net/p/rnnl/wiki/Home/)

25.  [char-rnn](https://github.com/karpathy/char-rnn)

26.  [MatConvNet: CNNs for MATLAB](https://github.com/vlfeat/matconvnet)

27.  [Minerva - a fast and flexible tool for deep learning on multi-GPU](https://github.com/dmlc/minerva)

28.  [Brainstorm - Fast, flexible and fun neural networks.](https://github.com/IDSIA/brainstorm)

29.  [Tensorflow - Open source software library for numerical computation using data flow graphs](https://github.com/tensorflow/tensorflow)

30.  [DMTK - Microsoft Distributed Machine Learning Tookit](https://github.com/Microsoft/DMTK)

31.  [Scikit Flow - Simplified interface for TensorFlow (mimicking Scikit Learn)](https://github.com/google/skflow)

32.  [MXnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning framework](https://github.com/dmlc/mxnet/)

33.  [Veles - Samsung Distributed machine learning platform](https://github.com/Samsung/veles)

34.  [Marvin - A Minimalist GPU-only N-Dimensional ConvNets Framework](https://github.com/PrincetonVision/marvin)

35.  [Apache SINGA - A General Distributed Deep Learning Platform](http://singa.incubator.apache.org/)

36.  [DSSTNE - Amazon's library for building Deep Learning models](https://github.com/amznlabs/amazon-dsstne)

37.  [SyntaxNet - Google's syntactic parser - A TensorFlow dependency library](https://github.com/tensorflow/models/tree/master/syntaxnet)

38.  [mlpack - A scalable Machine Learning library](http://mlpack.org/)

39.  [Torchnet - Torch based Deep Learning Library](https://github.com/torchnet/torchnet)

40.  [Paddle - PArallel Distributed Deep LEarning by Baidu](https://github.com/baidu/paddle)

41.  [NeuPy - Theano based Python library for ANN and Deep Learning](http://neupy.com)

42.  [Lasagne - a lightweight library to build and train neural networks in Theano](https://github.com/Lasagne/Lasagne)

43.  [nolearn - wrappers and abstractions around existing neural network libraries, most notably Lasagne](https://github.com/dnouri/nolearn)

44.  [Sonnet - a library for constructing neural networks by Google's DeepMind](https://github.com/deepmind/sonnet)

45.  [PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration](https://github.com/pytorch/pytorch)

46.  [CNTK - Microsoft Cognitive Toolkit](https://github.com/Microsoft/CNTK)

47.  [Serpent.AI - Game agent framework: Use any video game as a deep learning sandbox](https://github.com/SerpentAI/SerpentAI)

48.  [Caffe2 - A New Lightweight, Modular, and Scalable Deep Learning Framework](https://github.com/caffe2/caffe2)

49.  [deeplearn.js - Hardware-accelerated deep learning and linear algebra (NumPy) library for the web](https://github.com/PAIR-code/deeplearnjs)



### Miscellaneous



1.  [Google Plus - Deep Learning Community](https://plus.google.com/communities/112866381580457264725)

2.  [100 Best Github Resources in Github for DL](http://meta-guide.com/software-meta-guide/100-best-github-deep-learning/)

3.  [Word2Vec](https://code.google.com/p/word2vec/)

4. [Google deepdream - Neural Network art](https://github.com/google/deepdream)

5. [An efficient, batched LSTM.](https://gist.github.com/karpathy/587454dc0146a6ae21fc)

6. [A recurrent neural network designed to generate classical music.](https://github.com/hexahedria/biaxial-rnn-music-composition)

7. [AlphaGo - A replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search"](https://github.com/Rochester-NRT/AlphaGo)

8. [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers)

9. [Dockerface](https://github.com/natanielruiz/dockerface)



-----

### Contributing

Anything in mind that you think is awesome and would fit in this list? Feel free to send a [pull request](https://github.com/NEUChatbot/awesome-neuchatbot/pulls). 



-----

## License



[![CC0](http://i.creativecommons.org/p/zero/1.0/88x31.png)](http://creativecommons.org/publicdomain/zero/1.0/)



To the extent possible under law, [Xingqi Tang](https://www.linkedin.com/in/xingqi-tang-340204144/) has waived all copyright and related or neighboring rights to this work.