Habitat-Sim
A flexible, high-performance 3D simulator with configurable agents, multiple sensors, and generic 3D dataset handling (with built-in support for MatterPort3D, Gibson, Replica, and other datasets). When rendering a scene from the Matterport3D dataset, Habitat-Sim achieves several thousand frames per second (FPS) running single-threaded, and reaches over 10,000 FPS multi-process on a single GPU!
https://github.com/facebookresearch/habitat-sim/
A flexible, high-performance 3D simulator with configurable agents, multiple sensors, and generic 3D dataset handling (with built-in support for MatterPort3D, Gibson, Replica, and other datasets). When rendering a scene from the Matterport3D dataset, Habitat-Sim achieves several thousand frames per second (FPS) running single-threaded, and reaches over 10,000 FPS multi-process on a single GPU!
https://github.com/facebookresearch/habitat-sim/
GitHub
GitHub - facebookresearch/habitat-sim: A flexible, high-performance 3D simulator for Embodied AI research.
A flexible, high-performance 3D simulator for Embodied AI research. - facebookresearch/habitat-sim
Two interesting works on Unsupervised Object Detection & Tracking
http://e2crawfo.github.io/
http://e2crawfo.github.io/
e2crawfo.github.io
Eric Crawford
Website of Eric Crawford, PhD student studying machine learning and theoretical neuroscience.
New results in photo colorization
https://twitter.com/citnaj/status/1219156481762713602?s=19
https://twitter.com/citnaj/status/1219156481762713602?s=19
Twitter
Jason Antic
1/ This time I just have a single image here. It was taken in 1900 in New York City, and it's of course colorized by my latest and greatest and unreleased DeOldify model. I've done this one before but it's great to test the model on high resolution renders.
200 NLP datasets https://quantumstat.com/dataset/dataset.html
MOTS: Multi-Object Tracking and Segmentation https://www.vision.rwth-aachen.de/page/mots
If you missed this like me :) https://pytorch.org/mobile/home/
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Interactive segmentation
https://github.com/saic-vul/fbrs_interactive_segmentation
https://github.com/saic-vul/fbrs_interactive_segmentation
GitHub
GitHub - SamsungLabs/fbrs_interactive_segmentation: [CVPR2020] f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation…
[CVPR2020] f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation https://arxiv.org/abs/2001.10331 - GitHub - SamsungLabs/fbrs_interactive_segmentation: [CVPR2020] f-BRS: Rethink...
If you still not clear in how the backpropagation and model training work, then this tutorial for you.
https://learnml.today/making-backpropagation-autograd-mnist-classifier-from-scratch-in-Python-5
https://learnml.today/making-backpropagation-autograd-mnist-classifier-from-scratch-in-Python-5
learnml.today
Making Backpropagation, Autograd, MNIST Classifier from scratch in Python - LearnML.Today
Simple practical examples to give you a good understanding of how all this NN/AI things really work
OpenAI will use PyTorch as a standard now
https://openai.com/blog/openai-pytorch/
https://openai.com/blog/openai-pytorch/
Openai
OpenAI standardizes on PyTorch
We are standardizing OpenAI’s deep learning framework on PyTorch.
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TF published mutation operator for matrix compression with different methods like pruning, quantization, etc in real-time to minimize training process
https://blog.tensorflow.org/2020/02/matrix-compression-operator-tensorflow.html?m=1
https://blog.tensorflow.org/2020/02/matrix-compression-operator-tensorflow.html?m=1
blog.tensorflow.org
Matrix Compression Operator
The TensorFlow blog contains regular news from the TensorFlow team and the community, with articles on Python, TensorFlow.js, TF Lite, TFX, and more.
Interesting conference on March 14 in Kyiv — 8th Data Science UA Conference.
The most complicated algorithms, revolutionary inventions, and technologies all consist of hundreds of the simplest components that use create synergy together.
The ability to understand and create such projects depends on decomposition and simplification.
Register by the link >>> https://bit.ly/2Np7VGy
10% promotional code for our subscribers: ML_World
The most complicated algorithms, revolutionary inventions, and technologies all consist of hundreds of the simplest components that use create synergy together.
The ability to understand and create such projects depends on decomposition and simplification.
Register by the link >>> https://bit.ly/2Np7VGy
10% promotional code for our subscribers: ML_World
Video frames interpolation
https://sites.google.com/view/wenbobao/dain
https://sites.google.com/view/wenbobao/dain
Google
Wenbo Bao's Homepage - DAIN
Abstract
Video frame interpolation aims to synthesize non-existent frames in-between the original frames. While significant advances have been made from the deep convolutional neural networks, the quality of interpolation is often reduced due to large object…
Video frame interpolation aims to synthesize non-existent frames in-between the original frames. While significant advances have been made from the deep convolutional neural networks, the quality of interpolation is often reduced due to large object…
Step by step explained GPT-2 model
https://amaarora.github.io/2020/02/18/annotatedGPT2.html
https://amaarora.github.io/2020/02/18/annotatedGPT2.html
Committed towards better future
The Annotated GPT-2
Introduction Prerequisites Language Models are Unsupervised Multitask Learners Abstract Model Architecture (GPT-2) Model Specifications (GPT) Imports Transformer Decoder inside GPT-2 CONV1D Layer Explained FEEDFORWARD Layer Explained ATTENTION Layer Explained…
Machine Learning World pinned «Interesting conference on March 14 in Kyiv — 8th Data Science UA Conference. The most complicated algorithms, revolutionary inventions, and technologies all consist of hundreds of the simplest components that use create synergy together. The ability to…»
Now even your baby can learn about ML & NN 😂
https://www.amazon.com/Neural-Networks-Babies-Baby-University/dp/1492671207
https://www.amazon.com/Neural-Networks-Babies-Baby-University/dp/1492671207