New interesting paper about segmentation https://arxiv.org/pdf/1905.01892v1.pdf

#newbies
Collection of TF tutorials
https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ

Unsupervised scene decomposition
https://arxiv.org/abs/1901.11390

Fresh repo for Video2Depth prediction https://github.com/nianticlabs/monodepth2

+2.8M images with precise segmentation masks from Google https://ai.googleblog.com/2019/05/announcing-open-images-v5-and-iccv-2019.html?fbclid=IwAR1jiDKPtKVfZItxpZLMIYE8fS8n5PboLsB6VU5YRMlpBpQqhXPHnazrnVY

Очередной крутой пейпер от NVidia. На сей раз о собачках))
https://nvlabs.github.io/FUNIT/index.html

Did you already saw that? https://github.com/tensorflow/graphics

And here are two examples:
1) https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/6dof_alignment.ipynb
2) https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/reflectance.ipynb

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

Free Summer ML School in Kyiv(offline)
http://ml-school.railsreactor.com

Museum of Dali in Florida, used DeepFake model to create interactive video box with Dali.
"""Next step are movies.
https://youtu.be/BIDaxl4xqJ4

ICLR 2019 notes from David Abel @dabelcs

https://david-abel.github.io/notes/iclr_2019.pdf

Free book - Computer Age Statistical Inference - Algorithms, Evidence, & Data Science

Table of Content:
Part I Classic Statistical Inference
1 Algorithms and Inference
2 Frequentist Inference
3 Bayesian Inference
4 Fisherian Inference and Maximum Likelihood Estimation
5 Parametric Models and Exponential Families
Part II Early Computer-Age Methods
6 Empirical Bayes
7 James–Stein Estimation and Ridge Regression
8 Generalized Linear Models and Regression Trees
9 Survival Analysis and the EM Algorithm
10 The Jackknife and the Bootstrap
11 Bootstrap Confidence Intervals
12 Cross-Validation and Cp Estimates of Prediction Error
13 Objective Bayes Inference and MCMC
14 Postwar Statistical Inference and Methodology
Part III Twenty-First-Century Topics
15 Large-Scale Hypothesis Testing and FDRs
16 Sparse Modeling and the Lasso
17 Random Forests and Boosting
18 Neural Networks and Deep Learning
19 Support-Vector Machines and Kernel Methods
20 Inference After Model Selection
21 Empirical Bayes Estimation Strategies

Slides about PyTorch internals http://web.mit.edu/~ezyang/Public/pytorch-internals.pdf

One of the most famous persons in the world, while most of people even don't know her name :)

Interesting and resultative type of training/regularization
https://arxiv.org/pdf/1710.09412.pdf
Demo code here: https://github.com/facebookresearch/mixup-cifar10

Talking heads based on few shot learning

https://arxiv.org/abs/1905.08233

Awesome idea to make Depth prediction more accurate from Google guys
https://ai.googleblog.com/2019/05/moving-camera-moving-people-deep.html

One more paper that tries to move classification problem from supervised to unsupervised area https://arxiv.org/abs/1905.09272