Illustrated Deep Learning cheatsheets covering Stanford's CS 230 class

Set of illustrated Deep Learning cheatsheets covering the content of Stanford's CS 230 class:

Convolutional Neural Networks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks

Recurrent Neural Networks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks

Tips and tricks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-deep-learning-tips-and-tricks

Understanding Neural Networks via Feature Visualization: A survey
Nguyen et al.: https://arxiv.org/pdf/1904.08939v1.pdf
#neuralnetworks #generatornetwork #generativemodels

Unsupervised Learning with Graph Neural Networks
By Thomas Kipf.
Slides : https://helper.ipam.ucla.edu/publications/glws4/glws4_15546.pdf
Recording: https://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule
#deeplearning #neuralnetworks #unsupervisedlearning #technology

Path-Augmented Graph Transformer Network
Chen et al.: https://arxiv.org/abs/1905.12712
#ArtificialIntelligence #DeepLearning #MachineLearning

Video from Stills: Lensless Imaging with Rolling Shutter
Antipa et al.: https://arxiv.org/abs/1905.13221v1
#ArtificialIntelligence #DeepLearning #MachineLearning

On Conditioning GANs to Hierarchical Ontologies.) arxiv.org/abs/1905.06586

Important paper from Zellers et al. - "Defending Against Neural Fake News": arxiv.org/abs/1905.12616

Great to see more technical work on this topic, as well as further discussion of appropriate language model publication norms.

A Brief Introduction to Machine Learning for Engineers
By Osvaldo Simeone: https://arxiv.org/abs/1709.02840
#ArtificialIntelligence #Engineering #MachineLearning #NeuralNetworks

The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial
Benyamin Ghojogh and Mark Crowley : https://arxiv.org/abs/1905.12787
#ArtificialIntelligence #DeepLearning #MachineLearning

A Guide for Making Black Box Models Explainable
By Christoph Molnar: https://christophm.github.io/interpretable-ml-book/
#ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks

Notes from Karpathy on common mistakes when training NN
https://karpathy.github.io/2019/04/25/recipe/

CompILE: Compositional Imitation Learning and Execution
Kipf et al.: https://arxiv.org/abs/1812.01483

Code: https://github.com/tkipf/compile

#ArtificialIntelligence #DeepLearning #MachineLearning

DeepFake:
- not as effective as you might think.
- more easily detectable as you might think.
Which may explain why trolls and propagandists haven't used it much.


https://www.theverge.com/2019/3/5/18251736/deepfake-propaganda-misinformation-troll-video-hoax

The Beijing Academy of Artificial Intelligence publishes AI ethics guidelines.
Yes, the protection of individual privacy is mentioned.

Commentary at MIT Tech Review: https://www.technologyreview.com/s/613610/why-does-china-suddenly-care-about-ai-ethics-and-privacy/

Deep Convolutional Networks as Shallow Gaussian Processes #iclr2019
By @AdriGarriga

The kernel equivalent to a 32-layer ResNet obtains 0.84% classification error on
MNIST, SoTA for GPs with comparable params size

Github
https://github.com/convnets-as-gps/convnets-as-gps
ArXiv
https://arxiv.org/abs/1808.05587

CS 294-112. Deep Reinforcement Learning by Sergey Levine. UC Berkeley. Fall 2018

Video Lectures: https://www.youtube.com/playlist?list=PLkFD6_40KJIxJMR-j5A1mkxK26gh_qg37

Lecture Slides: https://rail.eecs.berkeley.edu/deeprlcourse/

On the Fairness of Disentangled Representations
Locatello et al.: https://arxiv.org/abs/1905.13662
#ArtificialIntelligence #DeepLearning #MachineLearning

Luck Matters: Understanding Training Dynamics of Deep ReLU Networks
Tian et al.: https://arxiv.org/abs/1905.13405
#ArtificialIntelligence #DeepLearning #MachineLearning

Geoffrey Hinton Leads Google Brain Representation Similarity Index Research Aiming to Understand…

https://medium.com/syncedreview/geoffrey-hinton-leads-google-brain-representation-similarity-index-research-aiming-to-understand-b5d14bf77f49

New algorithm may help people store more pictures, share videos faster
https://news.psu.edu/story/576002/2019/05/29/research/new-algorithm-may-help-people-store-more-pictures-share-videos

How to create your first Sequential model in Python (w/ Py code) using Colab (link: https://github.com/gcosma/DeepLearningTutorials/blob/master/SimpleSequentialModelColab.ipynb) #DataScience #DeepLearning #MachineLearning #AI
@GoogleColab