Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
Github: https://github.com/xinntao/Real-ESRGAN
Paper: https://arxiv.org/abs/2107.10833v1
How to Train Real-ESRGAN: https://github.com/xinntao/Real-ESRGAN/blob/master/Training.md
Colab Demo: https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo
👉 @bigdata_1
Github: https://github.com/xinntao/Real-ESRGAN
Paper: https://arxiv.org/abs/2107.10833v1
How to Train Real-ESRGAN: https://github.com/xinntao/Real-ESRGAN/blob/master/Training.md
Colab Demo: https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo
👉 @bigdata_1
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Improving Graph Convolutional Networks with Lessons from Transformers
https://blog.einstein.ai/improving-graph-networks-with-transformers/
👉 @bigdata_1
https://blog.einstein.ai/improving-graph-networks-with-transformers/
👉 @bigdata_1
👍4
AAVAE: Augmentation-Augmented Variational Autoencoders
Github: https://github.com/gridai-labs/aavae
Paper: https://arxiv.org/abs/2107.12329v1
Dataset: https://paperswithcode.com/dataset/cifar-10
👉 @bigdata_1
Github: https://github.com/gridai-labs/aavae
Paper: https://arxiv.org/abs/2107.12329v1
Dataset: https://paperswithcode.com/dataset/cifar-10
👉 @bigdata_1
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Guide To GPyTorch: A Python Library For Gaussian Process Models
https://analyticsindiamag.com/guide-to-gpytorch-a-python-library-for-gaussian-process-models/
👉 @bigdata_1
https://analyticsindiamag.com/guide-to-gpytorch-a-python-library-for-gaussian-process-models/
👉 @bigdata_1
👍3
RLQP: Accelerating Quadratic Optimization with RL
Github: https://github.com/berkeleyautomation/rlqp
Paper: https://arxiv.org/abs/2107.10833v1
👉 @bigdata_1
Github: https://github.com/berkeleyautomation/rlqp
Paper: https://arxiv.org/abs/2107.10833v1
👉 @bigdata_1
👍3
Know your data much faster with the new Sweetviz Python library
https://www.kdnuggets.com/2021/03/know-your-data-much-faster-sweetviz-python-library.html
👉 @bigdata_1
https://www.kdnuggets.com/2021/03/know-your-data-much-faster-sweetviz-python-library.html
👉 @bigdata_1
👍3
Introducing Triton: Open-Source GPU Programming for Neural Networks
https://openai.com/blog/triton/
Github: https://github.com/openai/triton
Documents: https://triton-lang.org/
👉 @bigdata_1
https://openai.com/blog/triton/
Github: https://github.com/openai/triton
Documents: https://triton-lang.org/
👉 @bigdata_1
👍4
AUTOMATIC INTEGRATION
http://www.computationalimaging.org/publications/automatic-integration/
👉 @bigdata_1
http://www.computationalimaging.org/publications/automatic-integration/
👉 @bigdata_1
👍3
Contextual Transformer Networks for Visual Recognition.
Github: https://github.com/JDAI-CV/CoTNet
Paper: https://arxiv.org/abs/2107.12292v1
Chalange: https://eval.ai/web/challenges/challenge-page/1041/leaderboard/2695
👉 @bigdata_1
Github: https://github.com/JDAI-CV/CoTNet
Paper: https://arxiv.org/abs/2107.12292v1
Chalange: https://eval.ai/web/challenges/challenge-page/1041/leaderboard/2695
👉 @bigdata_1
👍3
Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve Backbones
Github: https://github.com/PaddlePaddle/PaddleClas
Paper: https://arxiv.org/abs/2103.05959v1
👉 @bigdata_1
Github: https://github.com/PaddlePaddle/PaddleClas
Paper: https://arxiv.org/abs/2103.05959v1
👉 @bigdata_1
GitHub
GitHub - PaddlePaddle/PaddleClas: A treasure chest for visual classification and recognition powered by PaddlePaddle
A treasure chest for visual classification and recognition powered by PaddlePaddle - PaddlePaddle/PaddleClas
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Введение в Машинное Обучение и Data Science
Введение в методики Машинного Обучения и Data Science
Разведочный Анализ Данных (Exploratory Data Analysis, EDA)
Категориальные признаки (разведочный анализ данных)
Разделение Данных и Метрики
Необходимая Теория (Часть 1)
Необходимая Теория (Часть 2)
Полный Пайплайн (Pipeline)
Простая Линейная Регрессия
Множественная Линейная Регрессия
Логистическая Регрессия
Полный курс на youtube
👉 @bigdata_1
Введение в методики Машинного Обучения и Data Science
Разведочный Анализ Данных (Exploratory Data Analysis, EDA)
Категориальные признаки (разведочный анализ данных)
Разделение Данных и Метрики
Необходимая Теория (Часть 1)
Необходимая Теория (Часть 2)
Полный Пайплайн (Pipeline)
Простая Линейная Регрессия
Множественная Линейная Регрессия
Логистическая Регрессия
Полный курс на youtube
👉 @bigdata_1
👍2🔥1
Deepmind's Generally capable agents emerge from open-ended play
Blog : https://deepmind.com/blog/article/generally-capable-agents-emerge-from-open-ended-play
Paper: https://deepmind.com/research/publications/open-ended-learning-leads-to-generally-capable-agents
DeepMind Research: https://github.com/deepmind/deepmind-research
Video: https://www.youtube.com/watch?v=lTmL7jwFfdw&ab_channel=DeepMind
👉 @bigdata_1
Blog : https://deepmind.com/blog/article/generally-capable-agents-emerge-from-open-ended-play
Paper: https://deepmind.com/research/publications/open-ended-learning-leads-to-generally-capable-agents
DeepMind Research: https://github.com/deepmind/deepmind-research
Video: https://www.youtube.com/watch?v=lTmL7jwFfdw&ab_channel=DeepMind
👉 @bigdata_1
👍4
TimeSformer: A new architecture for video understanding
https://ai.facebook.com/blog/timesformer-a-new-architecture-for-video-understanding/
Paper: https://arxiv.org/abs/2102.05095
👉 @bigdata_1
https://ai.facebook.com/blog/timesformer-a-new-architecture-for-video-understanding/
Paper: https://arxiv.org/abs/2102.05095
👉 @bigdata_1
👍3
Droidlet: modular, heterogenous, multi-modal agents
Github: https://github.com/facebookresearch/droidlet
Paper: https://arxiv.org/abs/2101.10384
Article: https://yangx.top/machinelearning_ru/279
👉 @bigdata_1
Github: https://github.com/facebookresearch/droidlet
Paper: https://arxiv.org/abs/2101.10384
Article: https://yangx.top/machinelearning_ru/279
👉 @bigdata_1
👍2
ReDet: A Rotation-equivariant Detector for Aerial Object Detection
Github: https://github.com/csuhan/ReDet
Paper: https://arxiv.org/abs/2103.07733v1
👉 @bigdata_1
Github: https://github.com/csuhan/ReDet
Paper: https://arxiv.org/abs/2103.07733v1
👉 @bigdata_1
👍1
Pretrained Language Model
AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models
Github: https://github.com/huawei-noah/Pretrained-Language-Model
Paper: https://arxiv.org/abs/2107.13686v1
AutoTinyBERT: https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/AutoTinyBERT
👉 @bigdata_1
AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models
Github: https://github.com/huawei-noah/Pretrained-Language-Model
Paper: https://arxiv.org/abs/2107.13686v1
AutoTinyBERT: https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/AutoTinyBERT
👉 @bigdata_1
👍1
Contactless Sleep Sensing in Nest Hub
http://ai.googleblog.com/2021/03/contactless-sleep-sensing-in-nest-hub.html
👉 @bigdata_1
http://ai.googleblog.com/2021/03/contactless-sleep-sensing-in-nest-hub.html
👉 @bigdata_1
Googleblog
Contactless Sleep Sensing in Nest Hub
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StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators
Github: https://github.com/rinongal/StyleGAN-nada
Paper: https://arxiv.org/abs/2108.00946v1
Project: https://stylegan-nada.github.io/
Dataset: https://paperswithcode.com/dataset/lsun
👉 @bigdata_1
Github: https://github.com/rinongal/StyleGAN-nada
Paper: https://arxiv.org/abs/2108.00946v1
Project: https://stylegan-nada.github.io/
Dataset: https://paperswithcode.com/dataset/lsun
👉 @bigdata_1
👍2
Natural Language Processing Pipelines, Explained
https://www.kdnuggets.com/2021/03/natural-language-processing-pipelines-explained.html
👉 @bigdata_1
https://www.kdnuggets.com/2021/03/natural-language-processing-pipelines-explained.html
👉 @bigdata_1
👍1
Google представил нейросеть для детекции туберкулеза на радиограммах
https://pubs.rsna.org/doi/10.1148/radiol.212213
👉 @bigdata_1
https://pubs.rsna.org/doi/10.1148/radiol.212213
👉 @bigdata_1
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🥑 DALL·E Mini
Generate images from a text prompt
Demo: https://huggingface.co/spaces/flax-community/dalle-mini
Github: https://github.com/borisdayma/dalle-mini
Paper: https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA
👉 @bigdata_1
Generate images from a text prompt
Demo: https://huggingface.co/spaces/flax-community/dalle-mini
Github: https://github.com/borisdayma/dalle-mini
Paper: https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA
👉 @bigdata_1
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