​The #NeurIPS AI for social good workshop is live. Check out the live stream if you can't make it in person

https://slideslive.com/38922118/joint-workshop-on-ai-for-social-good-1

🔗 Joint Workshop on AI for Social Good 1
The accelerating pace of intelligent systems research and real world deployment presents three clear challenges for producing "good" intelligent systems: (1) the research community lacks incentives...

​Autoencoder Neural Network for Anomaly Detection with Unlabeled Dataset

🔗 Autoencoder Neural Network for Anomaly Detection with Unlabeled Dataset
If you are trying to detect anomalies from an unlabeled dataset and you are worried about not having a labeled dataset, then here is an…

🎥 Building IMU-based Gesture Recognition! (awesome project!)
👁 1 раз ⏳ 556 сек.
An inertial measurement unit (IMU) is a device that can sense motion and orientation. If you combine IMUs with machine learning, you can detect gestures! For last Halloween, I built a magic wand that combines IMUs, machine learning, and DIY electronics to detect different gestures when waving the wand. In this talk, we’ll go over the steps I took to build the wand and how you can do it too! Hardware prototyping is becoming more accessible than ever for people without a traditional hardware engineering backg

🎥 Biological and Artificial Reinforcement Learning 1 | NeurIPS 2019
👁 1 раз ⏳ 2294 сек.
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🎥 Andrew Ng Interview with Father Of Deep Learning , Geoffrey Hinton |Andrew Ng and Geoffrey Hinton
👁 1 раз ⏳ 2403 сек.
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​Machine Learning Tutorial Suite - 90+ Free Tutorials

https://data-flair.training/blogs/machine-learning-tutorials-home/

🔗 Machine Learning Tutorial and Deep Learning | Machine Learning using Python - DataFlair
Machine learning tutorial library - Package of 90+ free machine learning tutorials to grab the knowledge with lots of projects, case studies, & examples

​Learn Machine Learning in 2020

🔗 Learn Machine Learning in 2020
Best books and courses about machine learning to start with

​Making Deepfake Tools Doesn’t Have to Be Irresponsible. Here’s How.

🔗 Making Deepfake Tools Doesn’t Have to Be Irresponsible. Here’s How.
It’s possible to limit the harm synthetic media tools might cause — but it won’t happen without effort

​Deploying Machine Learning Models as Data, not Code: omega|ml

🔗 Deploying Machine Learning Models as Data, not Code: omega|ml
The data science community is on a mission to find the optimal approach to deploying machine learning solutions.

🎥 How Machine Learning Drives the Deceptive World of Deepfakes
👁 1 раз ⏳ 452 сек.
Deepfakes are spreading fast, and while some have playful intentions, others can cause serious harm. We stepped inside this deceptive new world to see what experts are doing to catch this altered content.
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Chances are you’ve seen a deepfake; Donald Trump, Barack Obama, and Mark Zuckerberg have all been targets of the computer-generated replications.

A deepfake is a video or an audio clip where deep l

Коллеги, простите что отвлекаю, но правда, неделю уже ищу, схему электрическую для - OpenBCI, подскажите пожалуйста, где можно ее найти?

​Useful Models for Robot Learning
Slides by Marc Deisenroth : https://deisenroth.cc/talks/2019-12-14-neurips-ws.pdf
#ReinforcementLearning #Robotics #MetaLearning

🔗

​David Abel on Twitter

🔗 David Abel on Twitter
Just wrapped ! Notes here:

​Artist in the Cloud
Towards the summit of AI, art, and autonomy
Gene Kogan : https://medium.com/@genekogan/artist-in-the-cloud-8384824a75c7
#ArtificialIntelligence #MachineLearning #Cryptoeconomics #Decentralization #Art

🔗 Artist in the Cloud
Towards the summit of AI, art, and autonomy

​Reinforcement learning framework and toolkits (Gym and Unity)

🔗 Reinforcement learning framework and toolkits (Gym and Unity)
An introduction to the reinforcement learning framework, and the environments Cart-pole (Gym) and Banana Colector (Unity)

​Deep Learning for Computer Vision with Python Dr Adrian Rosebrock

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

📝 Deep_Learning_for_Computer_Vision_with_Python_Dr_Adrian_Rosebrock_2017_PDF_ENG.pdf - 💾27 660 308

​Guide to learn computer vision in 2020

🔗 Guide to learn computer vision in 2020
This post will focus on resourses, which I believe will boost your knowledge the most in the computer vision.

​https://ai.facebook.com/blog/deepfake-detection-challenge-launches-with-new-data-set-and-kaggle-site/

https://deepfakedetectionchallenge.ai/

🔗 Deepfake Detection Challenge launches with new data set and Kaggle site
We’ve launched the Deepfake Detection Challenge, an open, collaborative initiative to accelerate development of new technologies for detecting deepfakes and manipulated media.

​Few-shot Video-to-Video Synthesis

https://www.youtube.com/watch?v=8AZBuyEuDqc&feature=youtu.be

[Paper] 👉 https://arxiv.org/abs/1910.12713

[Video]👉https://www.youtube.com/watch?v=8AZBuyEuDqc&feature=youtu.be

Code👉https://github.com/NVlabs/few-shot-vid2vid

🔗 Few-shot Video-to-Video Synthesis
Video-to-video synthesis (vid2vid) aims at converting an input semantic video, such as videos of human poses or segmentation masks, to an output photorealistic video. While the state-of-the-art of vid2vid has advanced significantly, existing approaches share two major limitations. First, they are data-hungry. Numerous images of a target human subject or a scene are required for training. Second, a learned model has limited generalization capability. A pose-to-human vid2vid model can only synthesize poses of the single person in the training set. It does not generalize to other humans that are not in the training set. To address the limitations, we propose a few-shot vid2vid framework, which learns to synthesize videos of previously unseen subjects or scenes by leveraging few example images of the target at test time. Our model achieves this few-shot generalization capability via a novel network weight generation module utilizing an attention mechanism. We conduct extensive experimental validations with compar


🎥 Few-Shot Video-to-Video Synthesis (NeurIPS 2019)
👁 1 раз ⏳ 128 сек.
Few-shot photorealistic video-to-video translation. It can be used for generating human motions from poses, synthesizing people talking from edge maps, or turning semantic label maps into photo-realistic videos. For more details, please visit https://nvlabs.github.io/few-shot-vid2vid/.

​Paper: https://arxiv.org/abs/1912.05656
code: https://github.com/mkocabas/VIBE

🔗 mkocabas/VIBE
Reference implementation of "VIBE: Video Inference for Human Body Pose and Shape Estimation" - mkocabas/VIBE