Hyperparameter tuning with Keras Tuner
https://blog.tensorflow.org/2020/01/hyperparameter-tuning-with-keras-tuner.html
Github: https://github.com/keras-team/keras-tuner
Distributed Tuning: https://keras-team.github.io/keras-tuner/tutorials/distributed-tuning/
https://blog.tensorflow.org/2020/01/hyperparameter-tuning-with-keras-tuner.html
Github: https://github.com/keras-team/keras-tuner
Distributed Tuning: https://keras-team.github.io/keras-tuner/tutorials/distributed-tuning/
blog.tensorflow.org
Hyperparameter tuning with Keras Tuner
The TensorFlow blog contains regular news from the TensorFlow team and the community, with articles on Python, TensorFlow.js, TF Lite, TFX, and more.
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f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation
Code: https://github.com/saic-vul/fbrs_interactive_segmentation
Paper: https://arxiv.org/abs/2001.10331
Code: https://github.com/saic-vul/fbrs_interactive_segmentation
Paper: https://arxiv.org/abs/2001.10331
Uplift modeling tutorial
https://habr.com/ru/company/ru_mts/blog/485980/
Code example: https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb
https://habr.com/ru/company/ru_mts/blog/485980/
Code example: https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb
Хабр
Туториал по uplift моделированию. Часть 1
Команда Big Data МТС активно извлекает знания из имеющихся данных и решает большое количество задач для бизнеса. Один из типов задач машинного обучения, с которыми мы сталкиваемся – это задачи...
TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval
Github: https://github.com/jayleicn/TVRetrieval
PyTorch implementation of MultiModal Transformer (MMT), a method for multimodal (video + subtitle) captioning: https://github.com/jayleicn/TVCaption
Paper: https://arxiv.org/abs/2001.09099v1
Github: https://github.com/jayleicn/TVRetrieval
PyTorch implementation of MultiModal Transformer (MMT), a method for multimodal (video + subtitle) captioning: https://github.com/jayleicn/TVCaption
Paper: https://arxiv.org/abs/2001.09099v1
Statistical_Consequences_of_Fat.pdf
27.3 MB
📚Fresh book by Nassim Taleb
Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications
https://arxiv.org/abs/2001.10488
@ai_machinelearning_big_data
Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications
https://arxiv.org/abs/2001.10488
@ai_machinelearning_big_data
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Open Source Differentiable Computer Vision Library for PyTorch
https://kornia.org
Code: https://github.com/kornia/kornia
Paper: https://arxiv.org/abs/1910.02190v2
https://kornia.org
Code: https://github.com/kornia/kornia
Paper: https://arxiv.org/abs/1910.02190v2
Project DeepSpeech
A TensorFlow implementation of Baidu's DeepSpeech architecture
Code: https://github.com/mozilla/DeepSpeech
Tensorflow & Pytorch: https://github.com/DemisEom/SpecAugment
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition:
https://arxiv.org/pdf/1904.08779.pdf
A TensorFlow implementation of Baidu's DeepSpeech architecture
Code: https://github.com/mozilla/DeepSpeech
Tensorflow & Pytorch: https://github.com/DemisEom/SpecAugment
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition:
https://arxiv.org/pdf/1904.08779.pdf
GitHub
GitHub - mozilla/DeepSpeech: DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in…
DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers. - mozilla/DeepSpeech
Filter Sketch for Network Pruning
Framework of FilterSketch. The top displays the second-order covariance of the pre-trained CNN
Code: https://github.com/lmbxmu/FilterSketch
Paper: https://arxiv.org/abs/2001.08514v1
Framework of FilterSketch. The top displays the second-order covariance of the pre-trained CNN
Code: https://github.com/lmbxmu/FilterSketch
Paper: https://arxiv.org/abs/2001.08514v1
How to Configure XGBoost for Imbalanced Classification
https://machinelearningmastery.com/xgboost-for-imbalanced-classification/
https://machinelearningmastery.com/xgboost-for-imbalanced-classification/
Torch-Struct: Deep Structured Prediction Library
Code: https://github.com/harvardnlp/pytorch-struct
Paper: https://arxiv.org/abs/2002.00876v1
Fast, general, and tested differentiable structured prediction in PyTorch: https://nlp.seas.harvard.edu/pytorch-struct/
Code: https://github.com/harvardnlp/pytorch-struct
Paper: https://arxiv.org/abs/2002.00876v1
Fast, general, and tested differentiable structured prediction in PyTorch: https://nlp.seas.harvard.edu/pytorch-struct/
GitHub
GitHub - harvardnlp/pytorch-struct: Fast, general, and tested differentiable structured prediction in PyTorch
Fast, general, and tested differentiable structured prediction in PyTorch - harvardnlp/pytorch-struct
Using ‘radioactive data’ to detect if a data set was used for training
https://ai.facebook.com/blog/using-radioactive-data-to-detect-if-a-data-set-was-used-for-training/
Paper: https://arxiv.org/abs/2002.00937
https://ai.facebook.com/blog/using-radioactive-data-to-detect-if-a-data-set-was-used-for-training/
Paper: https://arxiv.org/abs/2002.00937
Facebook
Using ‘radioactive data’ to detect if a dataset was used for training
Facebook AI has developed a new technique to mark the images in a dataset, so that researchers can then determine if a particular machine learning model has been trained using those images.
Forwarded from Data Science
Agile Machine Learning.pdf
4.1 MB
Agile Machine Learning: Effective Machine Learning Inspired by the Agile Manifesto (2019)
@datascienceiot
@datascienceiot
🔥Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation
SelectionGAN for guided image-to-image translation, where we translate an input image into another while respecting an external semantic guidance
Code: : https://github.com/Ha0Tang/SelectionGAN
Paper: https://arxiv.org/abs/2002.01048v1
@ai_machinelearning_big_data
SelectionGAN for guided image-to-image translation, where we translate an input image into another while respecting an external semantic guidance
Code: : https://github.com/Ha0Tang/SelectionGAN
Paper: https://arxiv.org/abs/2002.01048v1
@ai_machinelearning_big_data
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CCMatrix: A billion-scale bitext data set for training translation models
CCMatrix is the largest data set of high-quality, web-based bitexts for training translation models
https://ai.facebook.com/blog/ccmatrix-a-billion-scale-bitext-data-set-for-training-translation-models/
Paper: https://arxiv.org/abs/1911.04944
Github: https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix
@ai_machinelearning_big_data
CCMatrix is the largest data set of high-quality, web-based bitexts for training translation models
https://ai.facebook.com/blog/ccmatrix-a-billion-scale-bitext-data-set-for-training-translation-models/
Paper: https://arxiv.org/abs/1911.04944
Github: https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix
@ai_machinelearning_big_data
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Mutual Information-based State-Control for Intrinsically Motivated Reinforcement Learning
Agent Learning Framework: https://github.com/HorizonRobotics/alf
Github: https://github.com/ruizhaogit/misc
Paper: https://arxiv.org/abs/2002.01963v1
Agent Learning Framework: https://github.com/HorizonRobotics/alf
Github: https://github.com/ruizhaogit/misc
Paper: https://arxiv.org/abs/2002.01963v1
The Annotated Transformer
The Transformer – a model that uses attention to boost the speed with which these models can be trained.
https://nlp.seas.harvard.edu/2018/04/03/attention.html
The Illustrated Transformer: https://jalammar.github.io/illustrated-transformer/
Habr: https://habr.com/ru/post/486358/
The Transformer – a model that uses attention to boost the speed with which these models can be trained.
https://nlp.seas.harvard.edu/2018/04/03/attention.html
The Illustrated Transformer: https://jalammar.github.io/illustrated-transformer/
Habr: https://habr.com/ru/post/486358/
TensorFlow Lattice: Flexible, controlled and interpretable ML
The library enables you to inject domain knowledge into the learning process through common-sense or policy-driven shape constraints.
https://blog.tensorflow.org/2020/02/tensorflow-lattice-flexible-controlled-and-interpretable-ML.html
Video: https://www.youtube.com/watch?v=ABBnNjbjv2Q&feature=emb_logo
Github: https://github.com/tensorflow/lattice
The library enables you to inject domain knowledge into the learning process through common-sense or policy-driven shape constraints.
https://blog.tensorflow.org/2020/02/tensorflow-lattice-flexible-controlled-and-interpretable-ML.html
Video: https://www.youtube.com/watch?v=ABBnNjbjv2Q&feature=emb_logo
Github: https://github.com/tensorflow/lattice
Recurrent Neural Networks (RNN) with Keras
Recurrent neural networks (RNN) are a class of neural networks that is powerful for modeling sequence data such as time series or natural language.
https://www.tensorflow.org/guide/keras/rnn
Source code: https://github.com/tensorflow/docs/blob/master/site/en/guide/keras/rnn.ipynb
Habr : https://habr.com/ru/post/487808/
Recurrent neural networks (RNN) are a class of neural networks that is powerful for modeling sequence data such as time series or natural language.
https://www.tensorflow.org/guide/keras/rnn
Source code: https://github.com/tensorflow/docs/blob/master/site/en/guide/keras/rnn.ipynb
Habr : https://habr.com/ru/post/487808/
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
Official PyTorch implementation of pre-print Unsupervised Discovery of Interpretable Directions in the GAN Latent
Code: https://github.com/anvoynov/GANLatentDiscovery
Paper: https://arxiv.org/abs/2002.03754
Official PyTorch implementation of pre-print Unsupervised Discovery of Interpretable Directions in the GAN Latent
Code: https://github.com/anvoynov/GANLatentDiscovery
Paper: https://arxiv.org/abs/2002.03754
