OpenAI Five Beats World Champion DOTA2 Team 2-0
https://www.youtube.com/watch?v=tfb6aEUMC04
https://www.youtube.com/watch?v=tfb6aEUMC04
YouTube
OpenAI Five Beats World Champion DOTA2 Team 2-0! 🤖
Check out Lambda Labs here: https://lambdalabs.com/papers
OpenAI's blog post: https://openai.com/blog/openai-five-finals/
Reddit AMA: https://old.reddit.com/r/DotA2/comments/bf49yk/hello_were_the_dev_team_behind_openai_five_we/
Reddit discussion on buybacks:…
OpenAI's blog post: https://openai.com/blog/openai-five-finals/
Reddit AMA: https://old.reddit.com/r/DotA2/comments/bf49yk/hello_were_the_dev_team_behind_openai_five_we/
Reddit discussion on buybacks:…
Forwarded from Artificial Intelligence
Generating Game of Thrones Characters Using StyleGAN
article: https://blog.nanonets.com/stylegan-got/
gitHub repo: https://github.com/iyaja/stylegan-encoder
article: https://blog.nanonets.com/stylegan-got/
gitHub repo: https://github.com/iyaja/stylegan-encoder
@computer_science_and_programming :
Welcome to the world of:
* #Artificial #Intelligence,
* #Deep #Learning,
* #Machine #Learning,
* #Data #Science
* #Python Programming language
* and more advanced research
You will find up-to-date books📚 links🔗 and more you wanted.
Join us and learn hot topics of Computer Science together.👇👇👇
@computer_science_and_programming
Welcome to the world of:
* #Artificial #Intelligence,
* #Deep #Learning,
* #Machine #Learning,
* #Data #Science
* #Python Programming language
* and more advanced research
You will find up-to-date books📚 links🔗 and more you wanted.
Join us and learn hot topics of Computer Science together.👇👇👇
@computer_science_and_programming
👍1
Introducing FastBert — A simple Deep Learning library for BERT Models
https://medium.com/huggingface/introducing-fastbert-a-simple-deep-learning-library-for-bert-models-89ff763ad384
https://medium.com/huggingface/introducing-fastbert-a-simple-deep-learning-library-for-bert-models-89ff763ad384
Medium
Introducing FastBert — A simple Deep Learning library for BERT Models
A simple to use Deep Learning library to build and deploy BERT models
How to Develop a Deep CNN to Classify Satellite Photos of the Amazon Rainforest
https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-to-classify-satellite-photos-of-the-amazon-rainforest/
https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-to-classify-satellite-photos-of-the-amazon-rainforest/
Detection Free Human Instance Segmentation using Pose2Seg and PyTorch
https://towardsdatascience.com/detection-free-human-instance-segmentation-using-pose2seg-and-pytorch-72f48dc4d23e
https://towardsdatascience.com/detection-free-human-instance-segmentation-using-pose2seg-and-pytorch-72f48dc4d23e
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Article: https://arxiv.org/pdf/1703.10593.pdf
PyTorch Code: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
Article: https://arxiv.org/pdf/1703.10593.pdf
PyTorch Code: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
GitHub
GitHub - junyanz/pytorch-CycleGAN-and-pix2pix: Image-to-Image Translation in PyTorch
Image-to-Image Translation in PyTorch. Contribute to junyanz/pytorch-CycleGAN-and-pix2pix development by creating an account on GitHub.
25 Excellent Machine Learning Open Datasets
https://opendatascience.com/25-excellent-machine-learning-open-datasets/
https://opendatascience.com/25-excellent-machine-learning-open-datasets/
Open Data Science - Your News Source for AI, Machine Learning & more
25 Excellent Machine Learning Open Datasets
Looking to work on some data, but can't collect your own? Here are 25 helpful machine learning open datasets to use today!
A Gentle Introduction to Object Recognition With Deep Learning
https://machinelearningmastery.com/object-recognition-with-deep-learning/
https://machinelearningmastery.com/object-recognition-with-deep-learning/
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Video: youtu.be/p1b5aiTrGzY
Paper: arxiv.org/abs/1905.08233
Video: youtu.be/p1b5aiTrGzY
Paper: arxiv.org/abs/1905.08233
YouTube
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Statement regarding the purpose and effect of the technology
(NB: this statement reflects personal opinions of the authors and not of their organizations)
We believe that telepresence technologies in AR, VR and other media are to transform the world in the…
(NB: this statement reflects personal opinions of the authors and not of their organizations)
We believe that telepresence technologies in AR, VR and other media are to transform the world in the…
Data-Efficient Image Recognition with Contrastive Predictive Coding
Article: https://arxiv.org/abs/1905.09272
Article: https://arxiv.org/abs/1905.09272
arXiv.org
Data-Efficient Image Recognition with Contrastive Predictive Coding
Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient...
Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction
http://ai.googleblog.com/2019/05/moving-camera-moving-people-deep.html
http://ai.googleblog.com/2019/05/moving-camera-moving-people-deep.html
research.google
Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction
Posted by Tali Dekel, Research Scientist and Forrester Cole, Software Engineer, Machine Perception The human visual system has a remarkable abili...
How to Perform Object Detection in Photographs Using Mask R-CNN with Keras
https://machinelearningmastery.com/how-to-perform-object-detection-in-photographs-with-mask-r-cnn-in-keras/
https://machinelearningmastery.com/how-to-perform-object-detection-in-photographs-with-mask-r-cnn-in-keras/
TensorWatch: a debugging and visualization tool designed for deep learning
https://github.com/microsoft/tensorwatch
https://github.com/microsoft/tensorwatch
GitHub
GitHub - microsoft/tensorwatch: Debugging, monitoring and visualization for Python Machine Learning and Data Science
Debugging, monitoring and visualization for Python Machine Learning and Data Science - microsoft/tensorwatch
Estimators, Loss Functions, Optimizers —Core of ML Algorithms
https://towardsdatascience.com/estimators-loss-functions-optimizers-core-of-ml-algorithms-d603f6b0161a]
https://towardsdatascience.com/estimators-loss-functions-optimizers-core-of-ml-algorithms-d603f6b0161a]
Medium
Estimators, Loss Functions, Optimizers —Core of ML Algorithms
In order to understand how a machine learning algorithm learns from data to predict an outcome, it is essential to understand the…
Torchvision 0.3: segmentation, detection models, new datasets
https://pytorch.org/blog/torchvision03/
https://pytorch.org/blog/torchvision03/
COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration
https://arxiv.org/abs/1905.09275
https://arxiv.org/abs/1905.09275
illustrated Artificial Intelligence cheatsheets covering the content of the CS 221 class
Link: https://stanford.edu/~shervine/teaching/cs-221/
Reflex-based models with Machine Learning: https://stanford.edu/~shervine/teaching/cs-221/cheatsheet-reflex-models
Link: https://stanford.edu/~shervine/teaching/cs-221/
Reflex-based models with Machine Learning: https://stanford.edu/~shervine/teaching/cs-221/cheatsheet-reflex-models
stanford.edu
Teaching - CS 221
Teaching page of Shervine Amidi, Graduate Student at Stanford University.
How degenerate is the parametrization of neural networks with the ReLU activation function?
https://arxiv.org/abs/1905.09803
https://arxiv.org/abs/1905.09803