Denoising Diffusion Probabilistic Models
Рigh quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics.
https://hojonathanho.github.io/diffusion/
Github: https://github.com/hojonathanho/diffusion
Paper: https://arxiv.org/abs/2006.11239
Рigh quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics.
https://hojonathanho.github.io/diffusion/
Github: https://github.com/hojonathanho/diffusion
Paper: https://arxiv.org/abs/2006.11239
Introducing a New Privacy Testing Library in TensorFlow
https://blog.tensorflow.org/2020/06/introducing-new-privacy-testing-library.html
Github: https://github.com/tensorflow/privacy
https://blog.tensorflow.org/2020/06/introducing-new-privacy-testing-library.html
Github: https://github.com/tensorflow/privacy
blog.tensorflow.org
Introducing a New Privacy Testing Library in TensorFlow
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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The NetHack Learning Environment
The NetHack Learning Environment (NLE) is a Reinforcement Learning environment based on NetHack 3.6.6. NLE is designed to provide a standard RL interface to the game, and comes with tasks that function as a first step to evaluate agents on this new environment.
Github: https://github.com/facebookresearch/nle
Paper: https://arxiv.org/abs/2006.13760v1
Project: https://nethack.org/
The NetHack Learning Environment (NLE) is a Reinforcement Learning environment based on NetHack 3.6.6. NLE is designed to provide a standard RL interface to the game, and comes with tasks that function as a first step to evaluate agents on this new environment.
Github: https://github.com/facebookresearch/nle
Paper: https://arxiv.org/abs/2006.13760v1
Project: https://nethack.org/
Enhance your TensorFlow Lite deployment with Firebase
https://blog.tensorflow.org/2020/06/enhance-your-tensorflow-lite-deployment-with-firebase.html
https://blog.tensorflow.org/2020/06/enhance-your-tensorflow-lite-deployment-with-firebase.html
blog.tensorflow.org
Enhance your TensorFlow Lite deployment with Firebase
Learn how to use Firebase to deploy your TensorFlow Lite models over-the-air, monitor performance in production, and A/B test multiple model versions.
Computer Vision using Tensorflow
https://levelup.gitconnected.com/computer-vision-using-tensorflow-946718d3c123
Full Code can be found on my Github
https://levelup.gitconnected.com/computer-vision-using-tensorflow-946718d3c123
Full Code can be found on my Github
Medium
Computer Vision using Tensorflow
Giving computers the ability to see through Machine Learning
Extracting the main trend in a dataset: the Sequencer algorithm
The Sequencer is an algorithm that attempts to reveal the main sequence in a dataset, if it exists.
https://sequencer.org/
Github: https://github.com/dalya/Sequencer
Paper: https://arxiv.org/abs/2006.13948v1
The Sequencer is an algorithm that attempts to reveal the main sequence in a dataset, if it exists.
https://sequencer.org/
Github: https://github.com/dalya/Sequencer
Paper: https://arxiv.org/abs/2006.13948v1
Unsupervised Discovery of Object Landmarks via Contrastive Learning
Approach is motivated by the phenomenon of the gradual emergence of invariance in the representation hierarchy of a deep network.
https://people.cs.umass.edu/~zezhoucheng/contrastive_landmark/
Code: https://github.com/cvl-umass/ContrastLandmark
Paper: https://arxiv.org/abs/2006.14787
Approach is motivated by the phenomenon of the gradual emergence of invariance in the representation hierarchy of a deep network.
https://people.cs.umass.edu/~zezhoucheng/contrastive_landmark/
Code: https://github.com/cvl-umass/ContrastLandmark
Paper: https://arxiv.org/abs/2006.14787
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SpineNet: A Novel Architecture for Object Detection Discovered with Neural Architecture Search
https://ai.googleblog.com/2020/06/spinenet-novel-architecture-for-object.html
Paper: https://arxiv.org/abs/1912.05027
https://ai.googleblog.com/2020/06/spinenet-novel-architecture-for-object.html
Paper: https://arxiv.org/abs/1912.05027
30 Largest TensorFlow Datasets for Machine Learning
https://lionbridge.ai/datasets/tensorflow-datasets-machine-learning/
https://lionbridge.ai/datasets/tensorflow-datasets-machine-learning/
9 Key Machine Learning Algorithms Explained in Plain English
https://www.freecodecamp.org/news/a-no-code-intro-to-the-9-most-important-machine-learning-algorithms-today/
https://www.freecodecamp.org/news/a-no-code-intro-to-the-9-most-important-machine-learning-algorithms-today/
freeCodeCamp.org
9 Key Machine Learning Algorithms Explained in Plain English
By Nick McCullum Machine learning is changing the world. Google uses machine learning to suggest search results to users. Netflix uses it to recommend movies for you to watch. Facebook uses machine learning to suggest people you may know. Machine lea...
Adversarial NLI: A New Benchmark for Natural Language Understanding
Facebook introduced a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure
https://ai.facebook.com/research/publications/adversarial-nli-a-new-benchmark-for-natural-language-understanding/
Dataset: https://github.com/facebookresearch/anli
Paper: https://arxiv.org/abs/1910.14599
@ai_machinelearning_big_data
Facebook introduced a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure
https://ai.facebook.com/research/publications/adversarial-nli-a-new-benchmark-for-natural-language-understanding/
Dataset: https://github.com/facebookresearch/anli
Paper: https://arxiv.org/abs/1910.14599
@ai_machinelearning_big_data
Facebook
Adversarial NLI: A New Benchmark for Natural Language Understanding | Meta AI Research
We introduce a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure. We show that...
PyTorch Multi-GPU Metrics Library and More in New PyTorch Lightning Release
https://www.kdnuggets.com/2020/07/pytorch-multi-gpu-metrics-library-pytorch-lightning.html
https://www.kdnuggets.com/2020/07/pytorch-multi-gpu-metrics-library-pytorch-lightning.html
KDnuggets
PyTorch Multi-GPU Metrics Library and More in New PyTorch Lightning Release - KDnuggets
PyTorch Lightning, a very light-weight structure for PyTorch, recently released version 0.8.1, a major milestone. With incredible user adoption and growth, they are continuing to build tools to easily do AI research.
Text Classification with PyTorch
A baseline model with LSTMs
Article: https://medium.com/@fer.neutron/text-classification-with-pytorch-7111dae111a6
Code: https://github.com/FernandoLpz/Text-Classification-LSTMs-PyTorch
@ai_machinelearning_big_data
A baseline model with LSTMs
Article: https://medium.com/@fer.neutron/text-classification-with-pytorch-7111dae111a6
Code: https://github.com/FernandoLpz/Text-Classification-LSTMs-PyTorch
@ai_machinelearning_big_data
Medium
Text Classification with LSTMs in PyTorch
A baseline model with LSTMs
Deep Single Image Manipulation
https://www.vision.huji.ac.il/deepsim/
Code: https://github.com/eliahuhorwitz/DeepSIM
Paper: https://arxiv.org/abs/2007.01289
https://www.vision.huji.ac.il/deepsim/
Code: https://github.com/eliahuhorwitz/DeepSIM
Paper: https://arxiv.org/abs/2007.01289
GitHub
GitHub - eliahuhorwitz/DeepSIM: Official PyTorch implementation of the paper: "DeepSIM: Image Shape Manipulation from a Single…
Official PyTorch implementation of the paper: "DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample" (ICCV 2021 Oral) - eliahuhorwitz/DeepSIM
4 Automatic Outlier Detection Algorithms in Python
https://machinelearningmastery.com/model-based-outlier-detection-and-removal-in-python/
@ai_machinelearning_big_data
https://machinelearningmastery.com/model-based-outlier-detection-and-removal-in-python/
@ai_machinelearning_big_data
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EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning
EagleEye achieves better pruning performance than all of the studied pruning algorithms in our experiments.
Github: https://github.com/anonymous47823493/EagleEye
Paper: https://arxiv.org/abs/2007.02491v1
EagleEye achieves better pruning performance than all of the studied pruning algorithms in our experiments.
Github: https://github.com/anonymous47823493/EagleEye
Paper: https://arxiv.org/abs/2007.02491v1
YOLOv5
YOLOv5 improves accessibility for realtime object detection.
https://blog.roboflow.ai/yolov5-is-here/
Tutorial: https://blog.roboflow.ai/how-to-train-yolov5-on-a-custom-dataset/
Github: https://github.com/ultralytics/yolov5
Colab : https://colab.research.google.com/drive/1gDZ2xcTOgR39tGGs-EZ6i3RTs16wmzZQ
Video: https://www.youtube.com/watch?v=MdF6x6ZmLAY&feature=youtu.be
YOLOv5 improves accessibility for realtime object detection.
https://blog.roboflow.ai/yolov5-is-here/
Tutorial: https://blog.roboflow.ai/how-to-train-yolov5-on-a-custom-dataset/
Github: https://github.com/ultralytics/yolov5
Colab : https://colab.research.google.com/drive/1gDZ2xcTOgR39tGGs-EZ6i3RTs16wmzZQ
Video: https://www.youtube.com/watch?v=MdF6x6ZmLAY&feature=youtu.be
Roboflow Blog
YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS
Less than 50 days after the release YOLOv4, YOLOv5 improves accessibility for realtime object detection.
June 29, YOLOv5 has released the first official version of the repository. We wrote a new deep dive on YOLOv5.
June 12, 8:08 AM CDT Update: In response…
June 29, YOLOv5 has released the first official version of the repository. We wrote a new deep dive on YOLOv5.
June 12, 8:08 AM CDT Update: In response…
TensorFlow 2 meets the Object Detection API
https://blog.tensorflow.org/2020/07/tensorflow-2-meets-object-detection-api.html
https://blog.tensorflow.org/2020/07/tensorflow-2-meets-object-detection-api.html
blog.tensorflow.org
TensorFlow 2 meets the Object Detection API
Object detection in TensorFlow 2, with SSD, MobileNet, RetinaNet, Faster R-CNN, Mask R-CNN, CenterNet, EfficientNet, and more.
Auto-Sklearn 2.0: The Next Generation
auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
Github: https://github.com/automl/auto-sklearn
Paper: https://arxiv.org/pdf/2007.04074.pdf
auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
Github: https://github.com/automl/auto-sklearn
Paper: https://arxiv.org/pdf/2007.04074.pdf
Calculus.pdf
38.8 MB
Free MIT Courses and book on Calculus: The Key to Understanding Deep Learning
Course: https://ocw.mit.edu/resources/res-18-005-highlights-of-calculus-spring-2010/
@ai_machinelearning_big_data
Course: https://ocw.mit.edu/resources/res-18-005-highlights-of-calculus-spring-2010/
@ai_machinelearning_big_data