List of Best Machine Learning Resources


https://medium.com/machine-learning-for-humans/how-to-learn-machine-learning-24d53bb64aa1

PyTorch implementation of the Leap Meta-Learner

GitHub: https://github.com/amzn/metalearn-leap

Paper by Flennerhag et al.: https://arxiv.org/abs/1812.01054

#MachineLearning #ArtificialIntelligence #TransferLearning

Learning Loss for Active Learning

Donggeun Yoo and In So Kweon: https://arxiv.org/abs/1905.03677

#ArtificialIntelligence #DeepLearning #NeuralNetworks

A Survey on Neural Architecture Search
Wistuba et al.: https://arxiv.org/abs/1905.01392
#MachineLearning #ComputerVision #EvolutionaryComputing

Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting

Sen et al.: https://arxiv.org/abs/1905.03806

#ArtificialIntelligence #DeepLearning #MachineLearning

Flowpoints
An intuitive approach to creating deep learning models
By Marius Brataas: https://github.com/mariusbrataas/flowpoints_ml#readme
#deeplearning #pytorch #machinelearning #python

"Top 8 trends from ICLR 2019"
By Chip Huyen: https://huyenchip.com/2019/05/12/top-8-trends-from-iclr-2019.html
#deeplearning #iclr2019 #technology

Adaptive Neural Trees
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition

190 Tutorials for young specialists
https://www.kaggle.com/kashnitsky/mlcourse/kernels

GAN Lab: Play with Generative Adversarial Networks (GANs) in your browser!
By created by Minsuk Kahng, Nikhil Thorat, Polo Chau, Fernanda Viégas, and Martin Wattenberg: https://poloclub.github.io/ganlab/
Research paper: https://minsuk.com/research/papers/kahng-ganlab-vast2018.pdf
#AI #ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks

ICLR 2019 MILA, Microsoft, and MIT Share Best Paper Honours https://medium.com/syncedreview/iclr-2019-mila-microsoft-and-mit-share-best-paper-honours-440675d5773e https://t.iss.one/ArtificialIntelligenceArticles

TensorFlow Model Optimization Toolkit — Pruning API

Blog by TensorFlow: https://medium.com/tensorflow/tensorflow-model-optimization-toolkit-pruning-api-42cac9157a6a

#MachineLearning #ModelOptimization #TensorFlow #DeepLearning #NeuralNet

Government of Canada creates Advisory Council on Artificial Intelligence
“Artificial intelligence has enormous potential to help us design the responsive digital services that Canadians demand, but it must be used ethically and responsibly. The Advisory Council on Artificial intelligence will give us essential expertise from across industry, academia and government to make sure we use AI in a way that is transparent, deliberate and accountable.” – The Honourable Joyce Murray, President of the Treasury Board and Minister of Digital Government
From Innovation, Science and Economic Development Canada: https://www.canada.ca/en/innovation-science-economic-development/news/2019/05/government-of-canada-creates-advisory-council-on-artificial-intelligence.html
#artificialintelligence #council #canada

Generative models in Tensorflow 2”

GitHub, by Tim Sainburg: https://github.com/timsainb/tensorflow2-generative-models/

#deeplearning #generativeadversarialnetworks #tensorflow #technology

PyTorch Internals

By Edward Z. Yang: https://web.mit.edu/~ezyang/Public/pytorch-internals.pdf

#deeplearning #artificialintelligence #pytorch

Matrices as Tensor Network Diagrams
By MATH3MA: https://www.math3ma.com/blog/matrices-as-tensor-network-diagrams
#matrix #matrices #tensors #vectors

Another feather added to legendary Prof.Yoshua Bengio !


https://syncedreview.com/2019/05/15/yoshua-bengio-to-lead-new-canadian-advisory-council-on-ai/

Computer Age Statistical Inference - Algorithms, Evidence, & Data Science (FREE book pdf for personal use) - Link Below

Download LINK --> https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf

Table of Contents

Part I. Classic Statistical Inference

1. Algorithms & Inference
2. Frequentist Inference
3. Bayesian Inference
4. Fisherian Inference & Maximum Likelihood Estimation
5. Parametric Models & Exponential Families

Part II. Early Computer-Age Methods

6. Empirical Bayes
7. James–Stein Estimation & Ridge Regression
8. Generalized Linear Models & Regression Trees
9. Survival Analysis & the EM Algorithm
10. The Jackknife & the Bootstrap
11. Bootstrap Confidence Intervals
12. Cross-Validation & Estimates of Prediction Error
13. Objective Bayes Inference & MCMC
14. Postwar Statistical Inference & Methodology

Part III. Twenty-First-Century Topics

15. Large-Scale Hypothesis Testing & FDRs
16. Sparse Modeling & the Lasso
17. Random Forests & Boosting
18. Neural Networks & Deep Learning
19. Support-Vector

ICML | 2019
Thirty-sixth International Conference on Machine Learning

#ICML2019 tutorials have been announced.

Schedule here:
https://icml.cc/Conferences/2019/Schedule

#ArtificialIntelligence #DeepLearning #MachineLearning