Advancing NLP with Efficient Projection-Based Model Architectures
https://ai.googleblog.com/2020/09/advancing-nlp-with-efficient-projection.html
Sequence Projection Models: https://github.com/tensorflow/models/tree/master/research/sequence_projection
https://ai.googleblog.com/2020/09/advancing-nlp-with-efficient-projection.html
Sequence Projection Models: https://github.com/tensorflow/models/tree/master/research/sequence_projection
Googleblog
Advancing NLP with Efficient Projection-Based Model Architectures
📸 Old Photo Restoration (Official PyTorch Implementation)
Restore old photos that suffer from severe degradation through a deep learning approace.
http://raywzy.com/Old_Photo/
Github: https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life
Paper: https://arxiv.org/pdf/2009.07047v1.pdf
Colab: https://colab.research.google.com/drive/1NEm6AsybIiC5TwTU_4DqDkQO0nFRB-uA
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Restore old photos that suffer from severe degradation through a deep learning approace.
http://raywzy.com/Old_Photo/
Github: https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life
Paper: https://arxiv.org/pdf/2009.07047v1.pdf
Colab: https://colab.research.google.com/drive/1NEm6AsybIiC5TwTU_4DqDkQO0nFRB-uA
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👍1
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Facebook AI Releases ‘Dynabench’, A Dynamic Benchmark Testing Platform For Machine Learning Systems
Articel: https://ai.facebook.com/blog/dynabench-rethinking-ai-benchmarking/
Project: https://dynabench.org/
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Articel: https://ai.facebook.com/blog/dynabench-rethinking-ai-benchmarking/
Project: https://dynabench.org/
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Bringing the Mona Lisa Effect to Life with TensorFlow.js
https://blog.tensorflow.org/2020/09/bringing-mona-lisa-effect-to-life-tensorflow-js.html
Github: https://github.com/emilyxxie/mona_lisa_eyes
Demo: https://monalisaeffect.com/
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https://blog.tensorflow.org/2020/09/bringing-mona-lisa-effect-to-life-tensorflow-js.html
Github: https://github.com/emilyxxie/mona_lisa_eyes
Demo: https://monalisaeffect.com/
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🔋 The Most Complete Guide to PyTorch for Data Scientists
https://www.kdnuggets.com/2020/09/most-complete-guide-pytorch-data-scientists.html
Code: https://github.com/MLWhiz/data_science_blogs/tree/master/pytorch_guide
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https://www.kdnuggets.com/2020/09/most-complete-guide-pytorch-data-scientists.html
Code: https://github.com/MLWhiz/data_science_blogs/tree/master/pytorch_guide
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KDnuggets
The Most Complete Guide to PyTorch for Data Scientists - KDnuggets
All the PyTorch functionality you will ever need while doing Deep Learning. From an Experimentation/Research Perspective.
Graph Normalization
Learning Graph Normalization for Graph Neural Networks
Github: https://github.com/cyh1112/GraphNormalization
Paper: https://arxiv.org/abs/2009.11746v1
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Learning Graph Normalization for Graph Neural Networks
Github: https://github.com/cyh1112/GraphNormalization
Paper: https://arxiv.org/abs/2009.11746v1
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CaGNet: Context-aware Feature Generation for Zero-shot Semantic Segmentation.
Github: https://github.com/bcmi/CaGNet-Zero-Shot-Semantic-Segmentation
Paper: https://arxiv.org/abs/2009.12232v1
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Github: https://github.com/bcmi/CaGNet-Zero-Shot-Semantic-Segmentation
Paper: https://arxiv.org/abs/2009.12232v1
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Seeing Theory
🎲 A visual introduction to probability and statistics
https://seeing-theory.brown.edu/index.html#4thPage
📗 Free book: https://seeing-theory.brown.edu/doc/seeing-theory.pdf
🎲 A visual introduction to probability and statistics
https://seeing-theory.brown.edu/index.html#4thPage
📗 Free book: https://seeing-theory.brown.edu/doc/seeing-theory.pdf
seeing-theory.brown.edu
Seeing Theory
A visual introduction to probability and statistics.
Utterance-level Dialogue Understanding: An Empirical Study
The recent abundance of conversational data on the Web and elsewhere calls for effective NLP systems for dialog understanding.
Github: https://github.com/declare-lab/conv-emotion
Paper: https://arxiv.org/abs/2009.13902v1
The recent abundance of conversational data on the Web and elsewhere calls for effective NLP systems for dialog understanding.
Github: https://github.com/declare-lab/conv-emotion
Paper: https://arxiv.org/abs/2009.13902v1
GitHub
GitHub - declare-lab/conv-emotion: This repo contains implementation of different architectures for emotion recognition in conversations.
This repo contains implementation of different architectures for emotion recognition in conversations. - declare-lab/conv-emotion
Forwarded from TensorFlow
Transfer learning with TensorFlow Hub | TensorFlow Core
https://www.tensorflow.org/tutorials/images/transfer_learning_with_hub
TensorFlow Hub : https://tfhub.dev/
Github: https://github.com/tensorflow/docs/blob/master/site/en/tutorials/images/transfer_learning_with_hub.ipynb
@tensorflowblog
https://www.tensorflow.org/tutorials/images/transfer_learning_with_hub
TensorFlow Hub : https://tfhub.dev/
Github: https://github.com/tensorflow/docs/blob/master/site/en/tutorials/images/transfer_learning_with_hub.ipynb
@tensorflowblog
TensorFlow
Transfer learning with TensorFlow Hub | TensorFlow Core
Rotated Binary Neural Network
Pytorch implementation of RBNN.
Github: https://github.com/lmbxmu/RBNN
Paper: https://arxiv.org/abs/2009.13055
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Pytorch implementation of RBNN.
Github: https://github.com/lmbxmu/RBNN
Paper: https://arxiv.org/abs/2009.13055
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aLRP Loss: A Ranking-based, Balanced Loss Function
Unifying Classification and Localisation in Object Detection.
💻 Github: https://github.com/kemaloksuz/aLRPLoss
📎 Dataset: https://cocodataset.org/#download
🗒 Paper: https://arxiv.org/abs/2009.13592v1
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Unifying Classification and Localisation in Object Detection.
💻 Github: https://github.com/kemaloksuz/aLRPLoss
📎 Dataset: https://cocodataset.org/#download
🗒 Paper: https://arxiv.org/abs/2009.13592v1
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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
@ai_machinelearning_big_data
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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
@ai_machinelearning_big_data