Why We Need DevOps for ML Data
https://tecton.ai/blog/devops-ml-data/
Хабр: https://habr.com/ru/company/itsumma/blog/500272/
https://tecton.ai/blog/devops-ml-data/
Хабр: https://habr.com/ru/company/itsumma/blog/500272/
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An Implementation of ERNIE For Language Understanding (including Pre-training models and Fine-tuning tools)
ERNIE 2.0 is a continual pre-training framework for language understanding in which pre-training tasks can be incrementally built and learned through multi-task learning.
ERNIE 2.0 from Baidu: https://github.com/PaddlePaddle/ERNIE
Dataset: https://gluebenchmark.com/tasks
Understanding Language using XLNet with autoregressive pre-training
https://medium.com/@zxiao2015/understanding-language-using-xlnet-with-autoregressive-pre-training-9c86e5bea443
ERNIE 2.0 is a continual pre-training framework for language understanding in which pre-training tasks can be incrementally built and learned through multi-task learning.
ERNIE 2.0 from Baidu: https://github.com/PaddlePaddle/ERNIE
Dataset: https://gluebenchmark.com/tasks
Understanding Language using XLNet with autoregressive pre-training
https://medium.com/@zxiao2015/understanding-language-using-xlnet-with-autoregressive-pre-training-9c86e5bea443
The Best Deep Learning Papers from the ICLR 2020 Conference
https://neptune.ai/blog/iclr-2020-deep-learning
https://neptune.ai/blog/iclr-2020-deep-learning
neptune.ai
Blog - neptune.ai
Blog for ML/AI practicioners with articles about LLMOps. You'll find here guides, tutorials, case studies, tools reviews, and more.
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research
Awesome Sentiment Analysis papers: https://github.com/declare-lab/awesome-sentiment-analysis
Paper: https://arxiv.org/abs/2005.00357v1
Awesome Sentiment Analysis papers: https://github.com/declare-lab/awesome-sentiment-analysis
Paper: https://arxiv.org/abs/2005.00357v1
GitHub
GitHub - declare-lab/awesome-sentiment-analysis: Reading list for Awesome Sentiment Analysis papers
Reading list for Awesome Sentiment Analysis papers - declare-lab/awesome-sentiment-analysis
Global explanations for discovering bias in data
Github: https://github.com/agamiko/gebi
Code: https://github.com/AgaMiko/GEBI/blob/master/notebooks/GEBI.ipynb
Paper: https://arxiv.org/abs/2005.02269v1
Github: https://github.com/agamiko/gebi
Code: https://github.com/AgaMiko/GEBI/blob/master/notebooks/GEBI.ipynb
Paper: https://arxiv.org/abs/2005.02269v1
Set of Machine Learning Python plugins for GIMP
This paper introduces GIMP-ML, a set of Python plugins for the widely popular GNU Image Manipulation Program (GIMP). It enables the use of recent advances in computer vision to the conventional image editing pipeline.
Github: https://github.com/kritiksoman/GIMP-ML
Paper: https://arxiv.org/abs/2004.13060
Demo: https://www.youtube.com/watch?v=HVwISLRow_0
This paper introduces GIMP-ML, a set of Python plugins for the widely popular GNU Image Manipulation Program (GIMP). It enables the use of recent advances in computer vision to the conventional image editing pipeline.
Github: https://github.com/kritiksoman/GIMP-ML
Paper: https://arxiv.org/abs/2004.13060
Demo: https://www.youtube.com/watch?v=HVwISLRow_0
TK & TKL - Efficient Transformer-based neural re-ranking models
TK employs a small number of low-dimensional Transformer layers to contextualize query and document word embeddings. TK scores the interactions of the contextualized representations with simple, yet effective soft-histograms based on the kernel-pooling technique .
Github: https://github.com/sebastian-hofstaetter/transformer-kernel-ranking
Paper: https://arxiv.org/abs/2005.04908v1
The Neural-IR-Explorer is a interactive exploration tool. It allows you to browse around the actual results of a neural re-ranking run
https://neural-ir-explorer.ec.tuwien.ac.at/
TK employs a small number of low-dimensional Transformer layers to contextualize query and document word embeddings. TK scores the interactions of the contextualized representations with simple, yet effective soft-histograms based on the kernel-pooling technique .
Github: https://github.com/sebastian-hofstaetter/transformer-kernel-ranking
Paper: https://arxiv.org/abs/2005.04908v1
The Neural-IR-Explorer is a interactive exploration tool. It allows you to browse around the actual results of a neural re-ranking run
https://neural-ir-explorer.ec.tuwien.ac.at/
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Fine-tuning ResNet with Keras, TensorFlow, and Deep Learning
In this tutorial, you will learn how to fine-tune ResNet using Keras, TensorFlow, and Deep Learning.
https://www.pyimagesearch.com/2020/04/27/fine-tuning-resnet-with-keras-tensorflow-and-deep-learning/
In this tutorial, you will learn how to fine-tune ResNet using Keras, TensorFlow, and Deep Learning.
https://www.pyimagesearch.com/2020/04/27/fine-tuning-resnet-with-keras-tensorflow-and-deep-learning/
Little Ball of Fur
Little Ball of Fur consists of methods to do sampling of graph structured data
Documentation : https://little-ball-of-fur.readthedocs.io/en/latest/#little-ball-of-fur-documentation
github: https://github.com/benedekrozemberczki/littleballoffur
paper: https://arxiv.org/abs/2005.05257v1
Little Ball of Fur consists of methods to do sampling of graph structured data
Documentation : https://little-ball-of-fur.readthedocs.io/en/latest/#little-ball-of-fur-documentation
github: https://github.com/benedekrozemberczki/littleballoffur
paper: https://arxiv.org/abs/2005.05257v1
GitHub
GitHub - benedekrozemberczki/littleballoffur: Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX…
Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020) - benedekrozemberczki/littleballoffur
1008 machine translation models, covering of 140 different languages
https://huggingface.co/models?search=Helsinki-NLP%2Fopus-mt
https://huggingface.co/models?search=Helsinki-NLP%2Fopus-mt
huggingface.co
Models - Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
FlowTron: Improved Text to Speech Engine from NVIDIA
Paper: https://arxiv.org/abs/2005.05957
Code: https://github.com/NVIDIA/flowtron
Paper: https://arxiv.org/abs/2005.05957
Code: https://github.com/NVIDIA/flowtron
GitHub
GitHub - NVIDIA/flowtron: Flowtron is an auto-regressive flow-based generative network for text to speech synthesis with control…
Flowtron is an auto-regressive flow-based generative network for text to speech synthesis with control over speech variation and style transfer - NVIDIA/flowtron
PyTorch version of Stable Baselines, improved implementations of reinforcement learning algorithms.
https://towardsdatascience.com/stable-baselines-a-fork-of-openai-baselines-reinforcement-learning-made-easy-df87c4b2fc82
Documentation: https://stable-baselines3.readthedocs.io
Githab: https://github.com/DLR-RM/stable-baselines3
Paper: https://arxiv.org/abs/2005.05719v1
https://towardsdatascience.com/stable-baselines-a-fork-of-openai-baselines-reinforcement-learning-made-easy-df87c4b2fc82
Documentation: https://stable-baselines3.readthedocs.io
Githab: https://github.com/DLR-RM/stable-baselines3
Paper: https://arxiv.org/abs/2005.05719v1
Medium
Stable Baselines: a Fork of OpenAI Baselines — Reinforcement Learning Made Easy
After several weeks of hard work, we are happy to announce the release of Stable Baselines, a set of implementations of Reinforcement Learning (RL) algorithms with a common interface, based on OpenAI…
An Ethical Application of Computer Vision and Deep Learning — Identifying Child Soldiers Through Automatic Age and Military Fatigue Detection
https://www.pyimagesearch.com/2020/05/11/an-ethical-application-of-computer-vision-and-deep-learning-identifying-child-soldiers-through-automatic-age-and-military-fatigue-detection/
https://www.pyimagesearch.com/2020/05/11/an-ethical-application-of-computer-vision-and-deep-learning-identifying-child-soldiers-through-automatic-age-and-military-fatigue-detection/
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Objects are the secret key to revealing the world between vision and language
https://www.microsoft.com/en-us/research/blog/objects-are-the-secret-key-to-revealing-the-world-between-vision-and-language/
https://www.microsoft.com/en-us/research/blog/objects-are-the-secret-key-to-revealing-the-world-between-vision-and-language/
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Single-Stage Semantic Segmentation from Image Labels
Github: https://github.com/visinf/1-stage-wseg
Paper: https://arxiv.org/abs/2005.08104
Github: https://github.com/visinf/1-stage-wseg
Paper: https://arxiv.org/abs/2005.08104
How to Use Quantile Transforms for Machine Learning
https://machinelearningmastery.com/quantile-transforms-for-machine-learning/
https://machinelearningmastery.com/quantile-transforms-for-machine-learning/
MachineLearningMastery.com
How to Use Quantile Transforms for Machine Learning - MachineLearningMastery.com
Numerical input variables may have a highly skewed or non-standard distribution. This could be caused by outliers in the data, multi-modal distributions, highly exponential distributions, and more. Many machine learning algorithms prefer or perform better…
👄 Lip2Wav
Generate high quality speech from only lip movements. This code is part of the paper: Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis
Demo: https://www.youtube.com/watch?v=HziA-jmlk_4
Github: https://github.com/Rudrabha/Lip2Wav
Paper: https://arxiv.org/abs/2005.08209v1
Generate high quality speech from only lip movements. This code is part of the paper: Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis
Demo: https://www.youtube.com/watch?v=HziA-jmlk_4
Github: https://github.com/Rudrabha/Lip2Wav
Paper: https://arxiv.org/abs/2005.08209v1
YouTube
[CVPR, 2020] Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis (CVPR, 2020)
This is a demonstration video for the following research paper.
Paper title: Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis.
Authors: Prajwal K R*, Rudrabha Mukhopadhyay*, Vinay Namboodiri, C V Jawahar.
* both authors have an equal…
Paper title: Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis.
Authors: Prajwal K R*, Rudrabha Mukhopadhyay*, Vinay Namboodiri, C V Jawahar.
* both authors have an equal…
Galaxy Zoo: Classifying Galaxies with Crowdsourcing and Active Learning
In this tutorial you will know how to use crowdsourcing and machine learning to investigate how galaxies evolve by classifying millions of galaxy images.
https://blog.tensorflow.org/2020/05/galaxy-zoo-classifying-galaxies-with-crowdsourcing-and-active-learning.html
Code: https://github.com/mwalmsley/galaxy-zoo-bayesian-cnn/blob/88604a63ef3c1bd27d30ca71e0efefca13bf72cd/zoobot/active_learning/acquisition_utils.py#L81
In this tutorial you will know how to use crowdsourcing and machine learning to investigate how galaxies evolve by classifying millions of galaxy images.
https://blog.tensorflow.org/2020/05/galaxy-zoo-classifying-galaxies-with-crowdsourcing-and-active-learning.html
Code: https://github.com/mwalmsley/galaxy-zoo-bayesian-cnn/blob/88604a63ef3c1bd27d30ca71e0efefca13bf72cd/zoobot/active_learning/acquisition_utils.py#L81
Free Live Course: Deep Learning with PyTorch
https://www.freecodecamp.org/news/free-deep-learning-with-pytorch-live-course/
video: https://www.youtube.com/watch?v=vo_fUOk-IKk
https://www.freecodecamp.org/news/free-deep-learning-with-pytorch-live-course/
video: https://www.youtube.com/watch?v=vo_fUOk-IKk
freeCodeCamp.org
Free Live Course: Deep Learning with PyTorch
Are you interested in learning about Deep Learning? We are hosting a free 6-week live course on our YouTube channel, starting Saturday, November 20th at 9:30 AM PST. Passively watching a video is often not enough to learn a software concept. You need...