From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering
Gitgub: https://github.com/HazyResearch/HypHC
Paper: https://arxiv.org/abs/2010.00402
Gitgub: https://github.com/HazyResearch/HypHC
Paper: https://arxiv.org/abs/2010.00402
Nested Cross-Validation in Python
https://www.kdnuggets.com/2020/10/nested-cross-validation-python.html
Code: https://github.com/omartinez182/data-science-notebooks/blob/master/Nested_Cross_Validation_in_Python.ipynb
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https://www.kdnuggets.com/2020/10/nested-cross-validation-python.html
Code: https://github.com/omartinez182/data-science-notebooks/blob/master/Nested_Cross_Validation_in_Python.ipynb
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KDnuggets
Key Machine Learning Technique: Nested Cross-Validation, Why and How, with Python code
Selecting the best performing machine learning model with optimal hyperparameters can sometimes still end up with a poorer performance once in production. This phenomenon might be the result of tuning the model and evaluating its performance on the same sets…
Introduction to Pytorch Code Examples
An overview of training, models, loss functions and optimizers
Free course: https://cs230.stanford.edu/blog/pytorch/
Lectures: https://cs230.stanford.edu/lecture/
Github: https://github.com/thanhhff/CS230-Deep-Learning
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An overview of training, models, loss functions and optimizers
Free course: https://cs230.stanford.edu/blog/pytorch/
Lectures: https://cs230.stanford.edu/lecture/
Github: https://github.com/thanhhff/CS230-Deep-Learning
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A new open source framework for automatic differentiation with graphs
https://ai.facebook.com/blog/a-new-open-source-framework-for-automatic-differentiation-with-graphs/
Github: https://github.com/facebookresearch/gtn
Paper: https://arxiv.org/abs/2010.01003
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https://ai.facebook.com/blog/a-new-open-source-framework-for-automatic-differentiation-with-graphs/
Github: https://github.com/facebookresearch/gtn
Paper: https://arxiv.org/abs/2010.01003
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Graph-based Neural Structured Learning in TFX
New learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs.
https://www.tensorflow.org/tfx/tutorials/tfx/neural_structured_learning
Article: https://blog.tensorflow.org/2020/10/neural-structured-learning-in-tfx.html
Neural Structured Learning: https://www.tensorflow.org/neural_structured_learning
Github: https://github.com/tensorflow/neural-structured-learning#videos-and-colab-tutorials
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New learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs.
https://www.tensorflow.org/tfx/tutorials/tfx/neural_structured_learning
Article: https://blog.tensorflow.org/2020/10/neural-structured-learning-in-tfx.html
Neural Structured Learning: https://www.tensorflow.org/neural_structured_learning
Github: https://github.com/tensorflow/neural-structured-learning#videos-and-colab-tutorials
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📂 Free course from Amazon
Machine Learning University: Accelerated Natural Language Processing Class.
Github: https://github.com/aws-samples/aws-machine-learning-university-accelerated-nlp
Video lectures: https://www.youtube.com/playlist?list=PL8P_Z6C4GcuWfAq8Pt6PBYlck4OprHXsw
Notebook: https://github.com/aws-samples/aws-machine-learning-university-accelerated-nlp/tree/master/notebooks
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Machine Learning University: Accelerated Natural Language Processing Class.
Github: https://github.com/aws-samples/aws-machine-learning-university-accelerated-nlp
Video lectures: https://www.youtube.com/playlist?list=PL8P_Z6C4GcuWfAq8Pt6PBYlck4OprHXsw
Notebook: https://github.com/aws-samples/aws-machine-learning-university-accelerated-nlp/tree/master/notebooks
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RF: Learning a General Radiance Field for 3D Scene Representation and Rendering
Powerful implicit neural function that can represent and render arbitrarily complex 3D scenes in a single network only from 2D observations.
Github: https://github.com/alextrevithick/GRF
Paper: https://arxiv.org/abs/2010.04595v1
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Powerful implicit neural function that can represent and render arbitrarily complex 3D scenes in a single network only from 2D observations.
Github: https://github.com/alextrevithick/GRF
Paper: https://arxiv.org/abs/2010.04595v1
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Partial FC
Distributed deep learning training framework for face recognition.
Github: https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc
Paper: https://arxiv.org/abs/2010.05222v1
Largest Face Recognition Dataset: https://www.dropbox.com/sh/gdix4jabzlwtk72/AAAXEItN1zwdo_tzOx5-QqHWa?dl=0
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Distributed deep learning training framework for face recognition.
Github: https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc
Paper: https://arxiv.org/abs/2010.05222v1
Largest Face Recognition Dataset: https://www.dropbox.com/sh/gdix4jabzlwtk72/AAAXEItN1zwdo_tzOx5-QqHWa?dl=0
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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
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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
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https://heartbeat.fritz.ai/optimizing-the-levenshtein-distance-for-measuring-text-similarity-35d5bcf58476
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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
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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
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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
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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
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❤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
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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
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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
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https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
Code: https://github.com/pytorch/tutorials/blob/master/beginner_source/transfer_learning_tutorial.py
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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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