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, Adjunct Lecturer 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
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/
Forwarded from Artificial Intelligence
Unsupervised Learning with Graph Neural Networks
video: http://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule
guide: http://helper.ipam.ucla.edu/publications/glws4/glws4_15546.pdf
video: http://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule
guide: http://helper.ipam.ucla.edu/publications/glws4/glws4_15546.pdf
IPAM
Workshop IV: Deep Geometric Learning of Big Data and Applications - IPAM
Augmented Neural ODEs
Github: https://github.com/EmilienDupont/augmented-neural-odes
Article: https://arxiv.org/abs/1904.01681
Github: https://github.com/EmilienDupont/augmented-neural-odes
Article: https://arxiv.org/abs/1904.01681
GitHub
GitHub - EmilienDupont/augmented-neural-odes: Pytorch implementation of Augmented Neural ODEs :sunflower:
Pytorch implementation of Augmented Neural ODEs :sunflower: - EmilienDupont/augmented-neural-odes
SimpleSelfAttention
The purpose of this repository is two-fold:
-demonstrate improvements brought by the use of a self-attention layer in an image -classification model.
introduce a new layer which I call SimpleSelfAttention
https://github.com/sdoria/SimpleSelfAttention
The purpose of this repository is two-fold:
-demonstrate improvements brought by the use of a self-attention layer in an image -classification model.
introduce a new layer which I call SimpleSelfAttention
https://github.com/sdoria/SimpleSelfAttention
GitHub
GitHub - sdoria/SimpleSelfAttention: A simpler version of the self-attention layer from SAGAN, and some image classification results.
A simpler version of the self-attention layer from SAGAN, and some image classification results. - sdoria/SimpleSelfAttention
How to Train an Object Detection Model to Find Kangaroos in Photographs (R-CNN with Keras)
https://machinelearningmastery.com/how-to-train-an-object-detection-model-with-keras/
https://machinelearningmastery.com/how-to-train-an-object-detection-model-with-keras/
MachineLearningMastery.com
How to Train an Object Detection Model with Keras - MachineLearningMastery.com
Object detection is a challenging computer vision task that involves predicting both where the objects are in the image and what type of objects were detected. The Mask Region-based Convolutional Neural Network, or Mask R-CNN, model is one of the state-of…
EfficientNets
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
link: https://arxiv.org/abs/1905.11946.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
link: https://arxiv.org/abs/1905.11946.
arXiv.org
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically...
Multi-Sample Dropout for Accelerated Training and Better Generalization
Link: https://arxiv.org/abs/1905.09788
Link: https://arxiv.org/abs/1905.09788
arXiv.org
Multi-Sample Dropout for Accelerated Training and Better Generalization
Dropout is a simple but efficient regularization technique for achieving better generalization of deep neural networks (DNNs); hence it is widely used in tasks based on DNNs. During training,...
A Gentle Introduction to Deep Learning for Face Recognition
https://machinelearningmastery.com/introduction-to-deep-learning-for-face-recognition/
https://machinelearningmastery.com/introduction-to-deep-learning-for-face-recognition/
How to Perform Face Detection with Deep Learning in Keras
https://machinelearningmastery.com/how-to-perform-face-detection-with-classical-and-deep-learning-methods-in-python-with-keras/
https://machinelearningmastery.com/how-to-perform-face-detection-with-classical-and-deep-learning-methods-in-python-with-keras/
Learning Perceptually-Aligned Representations via Adversarial Robustness
Article: https://arxiv.org/abs/1906.00945
Github: https://github.com/MadryLab/robust_representations
Article: https://arxiv.org/abs/1906.00945
Github: https://github.com/MadryLab/robust_representations
Forwarded from Artificial Intelligence
YouTube
DeepMind Made a Math Test For Neural Networks
📝 The paper "Analysing Mathematical Reasoning Abilities of Neural Models" is available here:
https://arxiv.org/abs/1904.01557
❤️ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers
🙏 We would like to thank our generous Patreon…
https://arxiv.org/abs/1904.01557
❤️ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers
🙏 We would like to thank our generous Patreon…
Introducing TensorNetwork, an Open Source Library for Efficient Tensor Calculations
http://ai.googleblog.com/2019/06/introducing-tensornetwork-open-source.html
http://ai.googleblog.com/2019/06/introducing-tensornetwork-open-source.html
research.google
Introducing TensorNetwork, an Open Source Library for Efficient Tensor Calculati
Posted by Chase Roberts, Research Engineer, Google AI and Stefan Leichenauer, Research Scientist, X Many of the world's toughest scientific chall...
Free COURSE. CS Deep Reinforcement Learning UC Berkeley
Video Lectures: https://www.youtube.com/playlist?list=PLkFD6_40KJIxJM..
Lecture Material: http://rail.eecs.berkeley.edu/deeprlcourse/
Video Lectures: https://www.youtube.com/playlist?list=PLkFD6_40KJIxJM..
Lecture Material: http://rail.eecs.berkeley.edu/deeprlcourse/
Youtube
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