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.
http://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.
http://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
👍1
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
http://www.vision.huji.ac.il/deepsim/
Code: https://github.com/eliahuhorwitz/DeepSIM
Paper: https://arxiv.org/abs/2007.01289
http://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
👍1
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
This media is not supported in your browser
VIEW IN TELEGRAM
Using machine learning in the browser to lip sync to your favorite songs
https://blog.tensorflow.org/2020/07/using-machine-learning-in-browser-to-lip-sync.html
MediaPipe Face Mesh: https://google.github.io/mediapipe/solutions/face_mesh.html
Github: https://github.com/google/mediapipe
https://blog.tensorflow.org/2020/07/using-machine-learning-in-browser-to-lip-sync.html
MediaPipe Face Mesh: https://google.github.io/mediapipe/solutions/face_mesh.html
Github: https://github.com/google/mediapipe
Fast and Accurate Neural CRF Constituency Parsing
To improve the parsing performance,hee introduced a new scoring architecture based on boundary representation and biaffine attention, and a beneficial dropout strategy.
Github: https://github.com/yzhangcs/parser
Paper: https://www.ijcai.org/Proceedings/2020/560
To improve the parsing performance,hee introduced a new scoring architecture based on boundary representation and biaffine attention, and a beneficial dropout strategy.
Github: https://github.com/yzhangcs/parser
Paper: https://www.ijcai.org/Proceedings/2020/560
Indoor SfMLearner
The unsupervised depth estimation task in indoor environments.
Github: https://github.com/svip-lab/Indoor-SfMLearner
Paper: https://arxiv.org/abs/2007.07696v1
The unsupervised depth estimation task in indoor environments.
Github: https://github.com/svip-lab/Indoor-SfMLearner
Paper: https://arxiv.org/abs/2007.07696v1