Forwarded from TensorFlow
Text generation with an RNN | TensorFlow Core
https://www.tensorflow.org/tutorials/text/text_generation
Github: https://github.com/tensorflow/docs/blob/master/site/en/tutorials/text/text_generation.ipynb
Colab: https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/text/text_generation.ipynb
@tensorflowblog
https://www.tensorflow.org/tutorials/text/text_generation
Github: https://github.com/tensorflow/docs/blob/master/site/en/tutorials/text/text_generation.ipynb
Colab: https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/text/text_generation.ipynb
@tensorflowblog
TensorFlow
Text generation with an RNN | TensorFlow
Free course: Deep Learning with Pytorch by Yann LeCun
En: https://atcold.github.io/pytorch-Deep-Learning
Ru: https://atcold.github.io/pytorch-Deep-Learning/ru/
GitHub: https://github.com/Atcold/pytorch-Deep-Learning
YouTube: https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq
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En: https://atcold.github.io/pytorch-Deep-Learning
Ru: https://atcold.github.io/pytorch-Deep-Learning/ru/
GitHub: https://github.com/Atcold/pytorch-Deep-Learning
YouTube: https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq
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Optimizing the Levenshtein Distance for Measuring Text Similarity
https://heartbeat.fritz.ai/optimizing-the-levenshtein-distance-for-measuring-text-similarity-35d5bcf58476
@ai_machinelearning_big_data
https://heartbeat.fritz.ai/optimizing-the-levenshtein-distance-for-measuring-text-similarity-35d5bcf58476
@ai_machinelearning_big_data
Medium
Optimizing the Levenshtein Distance for Measuring Text Similarity
Using vectors instead of matrices
An Empirical Analysis of Visual Features for Multiple Object Tracking in Urban Scenes
Github: https://github.com/Guepardow/Visual-features
Paper: https://arxiv.org/abs/2010.07881
Github: https://github.com/Guepardow/Visual-features
Paper: https://arxiv.org/abs/2010.07881
📹 Multi-modal Dense Video Captioning
new dense video captioning approach that is able to utilize any number of modalities for event description.
Project: https://v-iashin.github.io/bmt.html
Russian: https://habr.com/ru/company/ods/blog/515688/#6-multi-modal-dense-video-captioning
Code: https://github.com/v-iashin/mdvc
Paper: https://arxiv.org/abs/2005.08271
new dense video captioning approach that is able to utilize any number of modalities for event description.
Project: https://v-iashin.github.io/bmt.html
Russian: https://habr.com/ru/company/ods/blog/515688/#6-multi-modal-dense-video-captioning
Code: https://github.com/v-iashin/mdvc
Paper: https://arxiv.org/abs/2005.08271
🔥 The first AI model that translates 100 languages without relying on English data
https://ai.facebook.com/blog/introducing-many-to-many-multilingual-machine-translation/
Code: https://github.com/pytorch/fairseq/tree/master/examples/m2m_100
Paper: https://ai.facebook.com/research/publications/beyond-english-centric-multilingual-machine-translation
@ai_machinelearning_big_data
https://ai.facebook.com/blog/introducing-many-to-many-multilingual-machine-translation/
Code: https://github.com/pytorch/fairseq/tree/master/examples/m2m_100
Paper: https://ai.facebook.com/research/publications/beyond-english-centric-multilingual-machine-translation
@ai_machinelearning_big_data
Meta
The first AI model that translates 100 languages without relying on English data
Facebook AI is introducing M2M-100, the first multilingual machine translation model that can translate between any pair of 100 languages without relying on English data.
pySBD: Python Sentence Boundary Disambiguation (SBD)
is a rule-based sentence boundary detection module that works out-of-the-box.
Github: https://github.com/nipunsadvilkar/pySBD
Paper: https://arxiv.org/abs/2010.09657v1
@ai_machinelearning_big_data
is a rule-based sentence boundary detection module that works out-of-the-box.
Github: https://github.com/nipunsadvilkar/pySBD
Paper: https://arxiv.org/abs/2010.09657v1
@ai_machinelearning_big_data
❤1
An Example of Graph Convolutional Networks
https://blog.zakjost.com/post/gcn_citeseer/
Github: https://github.com/zjost/blog_code/tree/master/gcn_citeseer
Paper: https://arxiv.org/abs/1609.02907
@ai_machinelearning_big_data
https://blog.zakjost.com/post/gcn_citeseer/
Github: https://github.com/zjost/blog_code/tree/master/gcn_citeseer
Paper: https://arxiv.org/abs/1609.02907
@ai_machinelearning_big_data
Zak Jost
An Example of Graph Convolutional Networks | Zak Jost
Controlled experiments are run on the Citeseer citation graph to understand how GCNs work
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FermiNet: Quantum Physics and Chemistry from First Principles
https://deepmind.com/blog/article/FermiNet
Github: https://github.com/deepmind/ferminet
Paper: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.033429
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https://deepmind.com/blog/article/FermiNet
Github: https://github.com/deepmind/ferminet
Paper: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.033429
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TRANSFER LEARNING FOR COMPUTER VISION TUTORIAL WITH PYTORCH
https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
Code: https://github.com/pytorch/tutorials/blob/master/beginner_source/transfer_learning_tutorial.py
@ai_machinelearning_big_data
https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
Code: https://github.com/pytorch/tutorials/blob/master/beginner_source/transfer_learning_tutorial.py
@ai_machinelearning_big_data
Gated Linear Networks
Github: https://github.com/deepmind/deepmind-research/tree/master/gated_linear_networks
Paper: https://arxiv.org/abs/2010.12268v1
The Potential of Gated Linear Networks: https://towardsdatascience.com/the-potential-of-gated-linear-networks-for-online-learning-70ca5ea073a
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Github: https://github.com/deepmind/deepmind-research/tree/master/gated_linear_networks
Paper: https://arxiv.org/abs/2010.12268v1
The Potential of Gated Linear Networks: https://towardsdatascience.com/the-potential-of-gated-linear-networks-for-online-learning-70ca5ea073a
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📡 Athena is an open-source implementation of end-to-end speech processing engine
Github: https://github.com/athena-team/athena
Paper: https://arxiv.org/abs/2010.13991v1
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Github: https://github.com/athena-team/athena
Paper: https://arxiv.org/abs/2010.13991v1
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Dealing with Imbalanced Data in Machine Learning
https://www.kdnuggets.com/2020/10/imbalanced-data-machine-learning.html
Code: https://github.com/mwitiderrick/imbalanced-data
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https://www.kdnuggets.com/2020/10/imbalanced-data-machine-learning.html
Code: https://github.com/mwitiderrick/imbalanced-data
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KDnuggets
Dealing with Imbalanced Data in Machine Learning
This article presents tools & techniques for handling data when it's imbalanced.
Understanding of OpenSeq2Seq.
https://www.geeksforgeeks.org/understanding-of-openseq2seq/
Machine Translation: https://nvidia.github.io/OpenSeq2Seq/html/machine-translation.html
Speech Recognition: https://nvidia.github.io/OpenSeq2Seq/html/speech-recognition.html
https://www.geeksforgeeks.org/understanding-of-openseq2seq/
Machine Translation: https://nvidia.github.io/OpenSeq2Seq/html/machine-translation.html
Speech Recognition: https://nvidia.github.io/OpenSeq2Seq/html/speech-recognition.html
GeeksforGeeks
Understanding of OpenSeq2Seq - GeeksforGeeks
A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.
🧠 Distilling Prioritized Paths For One-Shot Neural Architecture Search
Github: https://github.com/microsoft/cream
Paper: https://arxiv.org/abs/2010.15821v1
Models-Google Drive: https://drive.google.com/drive/folders/1NLGAbBF9bA1IUAxKlk2VjgRXhr6RHvRW
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Github: https://github.com/microsoft/cream
Paper: https://arxiv.org/abs/2010.15821v1
Models-Google Drive: https://drive.google.com/drive/folders/1NLGAbBF9bA1IUAxKlk2VjgRXhr6RHvRW
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Why nonlinear activation functions improve ML performance – with TensorFlow example.
https://www.machinecurve.com/index.php/2020/10/29/why-nonlinear-activation-functions-improve-ml-performance-with-tensorflow-example/
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https://www.machinecurve.com/index.php/2020/10/29/why-nonlinear-activation-functions-improve-ml-performance-with-tensorflow-example/
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End-to-end Animal Image Matting
Github: https://github.com/JizhiziLi/animal-matting
Paper: https://arxiv.org/abs/2010.16188v1
Test dataset: https://github.com/JizhiziLi/animal-matting/tree/master/demo/
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Github: https://github.com/JizhiziLi/animal-matting
Paper: https://arxiv.org/abs/2010.16188v1
Test dataset: https://github.com/JizhiziLi/animal-matting/tree/master/demo/
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🙆♂️ Gesticulator: A framework for semantically-aware speech-driven gesture generation.
https://svito-zar.github.io/gesticulator/
Code: https://github.com/Svito-zar/gesticulator
Paper: https://arxiv.org/abs/2001.09326
Video: https://www.youtube.com/watch?v=VQ8he6jjW08&feature=youtu.be
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https://svito-zar.github.io/gesticulator/
Code: https://github.com/Svito-zar/gesticulator
Paper: https://arxiv.org/abs/2001.09326
Video: https://www.youtube.com/watch?v=VQ8he6jjW08&feature=youtu.be
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CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
Github: https://github.com/InterDigitalInc/CompressAI
Paper: https://arxiv.org/abs/2011.03029v1
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Github: https://github.com/InterDigitalInc/CompressAI
Paper: https://arxiv.org/abs/2011.03029v1
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