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

Meet the BIG AWS Dev Day Kyiv 2019!

What is it, #AWSDevDayKyiv?
It's a free, full-day technical event where new developers will learn about some of the hottest topics in cloud computing, and experienced developers can dive deep on AWS services.

The conference brings together the Ukrainian cloud computing community to connect, collaborate, and learn about AWS.

What makes AWS Dev Day so special?
• 10 strong technical specialists to share their experience
• 3 tracks with subject matters and specific themes
• Ask an AWS Architect, where you can get a 1:1 session with a member of the AWS Solutions Architect Team.

In this action-packed one-day event you can choose to attend 3 tracks:
☁ Modern App Development
☁ Machine Learning
☁ Backends & Architecture

Hot topics on the ML track: «Add intelligence to applications with AWS ML Services», «Build models for Amazon SageMaker», «Scaling ML from 0 to millions of users», «Building a Modern Data platform in the Cloud».

When?
Tuesday, June 11 | 9AM - 5PM

Where?
"Mercure", Vadyma Hetmana Street 6, Kyiv

Participation is free of charge. Please, fill in the registration form below.
👉 Register now: https://provectus.com/events/#event-11-June-2019-aws-dev-day-kyiv

Learn more: https://awsdevday.kyiv.provectus.com

#junior
Good introduction article about base things
https://towardsdatascience.com/estimators-loss-functions-optimizers-core-of-ml-algorithms-d603f6b0161a

From today our channel also be presented on FB and Twitter
FB: https://www.facebook.com/mlworld
Twitter: https://twitter.com/mlwcommunity

Great article on image enhancing (without NN!!!!)
https://sites.google.com/view/handheld-super-res/

Good overview article for 3D pose estimation
https://blog.nanonets.com/human-pose-estimation-3d-guide/

Model optimization with new Tensorflow tool
https://medium.com/tensorflow/tensorflow-model-optimization-toolkit-pruning-api-42cac9157a6a

Google creating next level translation app, Star Trek translator is not so far as we though)
https://www.technologyreview.com/s/613559/google-ai-language-translation/

Unsupervised Learning with Graph Neural Networks

Videos from workshop http://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule

Slides: http://helper.ipam.ucla.edu/publications/glws4/glws4_15546.pdf

Pytorch implementation of Augmented Neural ODEs

https://arxiv.org/abs/1904.01681
https://github.com/EmilienDupont/augmented-neural-odes

Interesting new model, faster and smaller the all before
https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet