Microsoft launches a drag-and-drop machine learning tool
Article by Frederic Lardinois: https://techcrunch.com/2019/05/02/microsoft-launches-a-drag-and-drop-machine-learning-tool-and-hosted-jupyter-notebooks/
#ArtificialIntelligence #DeepLearning #MachineLearning
Article by Frederic Lardinois: https://techcrunch.com/2019/05/02/microsoft-launches-a-drag-and-drop-machine-learning-tool-and-hosted-jupyter-notebooks/
#ArtificialIntelligence #DeepLearning #MachineLearning
TechCrunch
Microsoft launches a drag-and-drop machine learning tool
Microsoft today announced three new services that all aim to simplify the process of machine learning. These range from a new interface for a tool that completely automates the process of creating models, to a new no-code visual interface for building, training…
Datasheets for Datasets
Gebru et al.: https://arxiv.org/abs/1803.09010
#Databases #ArtificialIntelligence #AIEthics #Ethics #MachineLearning
Gebru et al.: https://arxiv.org/abs/1803.09010
#Databases #ArtificialIntelligence #AIEthics #Ethics #MachineLearning
arXiv.org
Datasheets for Datasets
The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains. To address this gap, we propose...
Minicourse in Deep Learning with PyTorch
By Alfredo Canziani: https://github.com/Atcold/pytorch-Deep-Learning-Minicourse
#DeepLearning #MachineLearning #PyTorch
By Alfredo Canziani: https://github.com/Atcold/pytorch-Deep-Learning-Minicourse
#DeepLearning #MachineLearning #PyTorch
GitHub
GitHub - Atcold/NYU-DLSP20: NYU Deep Learning Spring 2020
NYU Deep Learning Spring 2020. Contribute to Atcold/NYU-DLSP20 development by creating an account on GitHub.
State of the art video editing - make any object in a video invisible!
Deep Flow-Guided Video Inpainting
paper: https://www.profillic.com/paper/arxiv:1905.02884
Deep Flow-Guided Video Inpainting
paper: https://www.profillic.com/paper/arxiv:1905.02884
Profillic
Profillic: AI research & source code to supercharge your projects
Explore state-of-the-art in machine learning, AI, and robotics research. Browse papers, source code, models, and more by topics and authors. Connect with researchers and engineers working on related problems in machine learning, deep learning, natural language…
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
https://github.com/kmario23/deep-learning-drizzle
https://github.com/kmario23/deep-learning-drizzle
GitHub
GitHub - kmario23/deep-learning-drizzle: Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision…
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!! - kmario23/deep-learning-drizzle
Canada is Failing in Applied AI Innovation
"Canada is failing in Applied AI innovation and the impact will be severe unless everyone leads in new and more powerful intentionality ways."
Article by Dr. Cindy Gordon: https://cata.ca/2019/ai-innovation-lagging/
#ArtificialIntelligence #Canada #Governance
"Canada is failing in Applied AI innovation and the impact will be severe unless everyone leads in new and more powerful intentionality ways."
Article by Dr. Cindy Gordon: https://cata.ca/2019/ai-innovation-lagging/
#ArtificialIntelligence #Canada #Governance
Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making
Cai et al.: https://arxiv.org/abs/1902.02960
#humancentered #machinelearning #medical #innovation #technology
Cai et al.: https://arxiv.org/abs/1902.02960
#humancentered #machinelearning #medical #innovation #technology
DL app that turns UI screenshots into a Bootstrap implementation. https://news.developer.nvidia.com/ai-turns-ui-designs-into…/
The code is open-source! https://github.com/tonybeltramelli/pix2code
The code is open-source! https://github.com/tonybeltramelli/pix2code
Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit
Belinda Tzen and Maxim Raginsky: https://arxiv.org/abs/1905.09883
#ArtificialIntelligence #DifferentialEquation #MachineLearning
Belinda Tzen and Maxim Raginsky: https://arxiv.org/abs/1905.09883
#ArtificialIntelligence #DifferentialEquation #MachineLearning
New "Simple Self Attention" Layer
GitHub by Sébastien Doria: https://github.com/sdoria/SimpleSelfAttention
#MachineLearning #Pytorch #FastAI #SelfAttention
GitHub by Sébastien Doria: https://github.com/sdoria/SimpleSelfAttention
#MachineLearning #Pytorch #FastAI #SelfAttention
An Algorithmic Barrier to Neural Circuit Understanding
Venkatakrishnan Ramaswamy: https://www.biorxiv.org/content/10.1101/639724v1
#Algorithme #Neuroscience #innovation #technology
Venkatakrishnan Ramaswamy: https://www.biorxiv.org/content/10.1101/639724v1
#Algorithme #Neuroscience #innovation #technology
bioRxiv
An Algorithmic Barrier to Neural Circuit Understanding
Neuroscience is witnessing extraordinary progress in experimental techniques, especially at the neural circuit level. These advances are largely aimed at enabling us to understand how neural circuit computations mechanistically cause behavior. Here, using…
ArviZ: Exploratory analysis of Bayesian models
Includes functions for posterior analysis, sample diagnostics, model checking, and comparison: https://arviz-devs.github.io/arviz/
#ArtificialIntelligence #Bayesian #BayesianInference #MachineLearning #Python
Includes functions for posterior analysis, sample diagnostics, model checking, and comparison: https://arviz-devs.github.io/arviz/
#ArtificialIntelligence #Bayesian #BayesianInference #MachineLearning #Python
VAE-SBD
PyTorch implementation of the Variational Autoencoder with Spatial Broadcast Decoder.
GitHub by Daniel Daza: https://github.com/dfdazac/vaesbd
#deeplearning #pytorch #technology #innovation
PyTorch implementation of the Variational Autoencoder with Spatial Broadcast Decoder.
GitHub by Daniel Daza: https://github.com/dfdazac/vaesbd
#deeplearning #pytorch #technology #innovation
GitHub
GitHub - dfdazac/vaesbd: Variational Autoencoder with Spatial Broadcast Decoder
Variational Autoencoder with Spatial Broadcast Decoder - GitHub - dfdazac/vaesbd: Variational Autoencoder with Spatial Broadcast Decoder
A curated list of gradient boosting research papers with implementations.
https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers
https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers
GitHub
GitHub - benedekrozemberczki/awesome-gradient-boosting-papers: A curated list of gradient boosting research papers with implementations.
A curated list of gradient boosting research papers with implementations. - GitHub - benedekrozemberczki/awesome-gradient-boosting-papers: A curated list of gradient boosting research papers with ...
How to Perform Object Detection With YOLOv3 in Keras
https://machinelearningmastery.com/how-to-perform-object-detection-with-yolov3-in-keras/
https://machinelearningmastery.com/how-to-perform-object-detection-with-yolov3-in-keras/
MachineLearningMastery.com
How to Perform Object Detection With YOLOv3 in Keras - MachineLearningMastery.com
Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph. It is a challenging problem that involves building upon methods for object recognition (e.g. where are they)…
STAT479: Deep Learning (Spring 2019)
Instructor: Sebastian Raschka
Course material for STAT 479: Deep Learning (SS 2019) at University Wisconsin-Madison
https://github.com/rasbt/stat479-deep-learning-ss19
Instructor: Sebastian Raschka
Course material for STAT 479: Deep Learning (SS 2019) at University Wisconsin-Madison
https://github.com/rasbt/stat479-deep-learning-ss19
GitHub
GitHub - rasbt/stat479-deep-learning-ss19: Course material for STAT 479: Deep Learning (SS 2019) at University Wisconsin-Madison
Course material for STAT 479: Deep Learning (SS 2019) at University Wisconsin-Madison - rasbt/stat479-deep-learning-ss19
PyTorch prerequisites - Neural network programming series
http://deeplizard.com/learn/video/v5cngxo4mIg
http://deeplizard.com/learn/video/v5cngxo4mIg
Deeplizard
PyTorch Prerequisites - Syllabus for Neural Network Programming Course
Let's get ready to learn about neural network programming and PyTorch! In this video, we will look at the prerequisites needed to be best prepared. We'll get an overview of the series, and we'll get
Optimizing Steering Car Paths with PyTorch
http://blog.benwiener.com/programming/2019/05/14/steering-car.html
http://blog.benwiener.com/programming/2019/05/14/steering-car.html
Benwiener
Optimizing Steering Car Paths with PyTorch
Want to see another bizarre way to use PyTorch? If you've read some of my recent posts, you've seen the basic idea before. I'm using automatic differenti...
Handtrack.js: Hand Tracking Interactions in the Browser using Tensorflow.js and 3 lines of code
Blog by Victor Dibia: https://towardsdatascience.com/handtrackjs-677c29c1d585
#JavaScript #MachineLearning #ArtificialIntelligence #TensorFlow #TensorFlowJS
Blog by Victor Dibia: https://towardsdatascience.com/handtrackjs-677c29c1d585
#JavaScript #MachineLearning #ArtificialIntelligence #TensorFlow #TensorFlowJS
Medium
Handtrack.js: Hand Tracking Interactions in the Browser using Tensorflow.js and 3 lines of code.
Handtrack.js library allows you track a user’s hand (bounding box) from an image in any orientation, in 3 lines of code.