A visual introduction to machine learning.

It is an interactive website, which would be really useful to the beginners, as a perfect visual explanation of how decision trees work. It shows how one can go from statistical parametric evaluation to decision tree building.

Link: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/?utm_source=telegram&utm_medium=opendatascience

#decisiontrees #beginner #novice #firststep #howitworks

Model Tuning and the Bias-Variance Tradeoff
(part II of Visual Introduction to Machine Learning by r2d3)

Bias-Variance tradeoff happens because you have to find optimal balance between model being too simple and too complex. Too complex models tend to overfit — to become to adapted to the training data, so the results on the testing (new, unknown to model) data become less accurate. The article explains with the example from previous part how this actually works.

http://www.r2d3.us/visual-intro-to-machine-learning-part-2/?utm_source=telegram&utm_medium=opendatascience

#decisiontrees #beginner #novice #firststep #howitworks

Post on the estimation of the likelihood of the ads closing due to actually selling the good, which was the goal of Avito’s #Kaggle contest. Author built a #neuralnetwork solution and had 18th place.

https://towardsdatascience.com/kaggle-avito-demand-challenge-18th-place-solution-neural-network-ac19efd6e183

Nice hot start on data streaming with #Spark

https://towardsdatascience.com/learn-how-to-code-and-deploy-machine-learning-models-on-structured-streaming-564749840516

#scala #bigdata #streaming #kafka #tutorial #novice #beginner

Practical Advice for Building Deep Neural Networks

Some practical tips for training deep neural networks based the experiences (rooted mainly in TensorFlow). Some of the suggestions may seem obvious, but they weren’t at some point. Other suggestions may not apply or might even be bad advice for particular task: use discretion!

https://pcc.cs.byu.edu/2017/10/02/practical-advice-for-building-deep-neural-networks/

#neuralnetworks #dl #tensorflow

On the Adam optimizer convergence

Investigation of the convergence of popular optimization algorithms like Adam, RMSProp and propose new variants of these methods which provably converge to optimal solution in convex settings.

Link: https://openreview.net/forum?id=ryQu7f-RZ
PDF: https://openreview.net/pdf?id=ryQu7f-RZ

#iclr2018 #neuralnetworks #optimizers

Pitfalls of Batch Norm in TensorFlow and Sanity Checks for Training Networks

Some more practical advices on #tensorflow training with source code and reference links to look up.

https://medium.com/@theshank/pitfalls-of-batch-norm-in-tensorflow-and-sanity-checks-for-training-networks-e86c207548c8

#beginner #novice #dl #tutorial

Introduction to Python Ensembles

Ensemble is an approach for mixing algorithms to obtain strong sides of all the approaches.

https://www.kdnuggets.com/2018/02/introduction-python-ensembles.html

#ensemble #stacking #tutorial #beginnig #novice

Adversarial attack — type of input or a mask applied to the input of the machine learning model to make it wrong. It is a way to cheat with the output, to ‘fool’ the algorithm.

«Attacking Machine Learning with Adversarial Examples» at Open AI blog covers the basics and provides some examples.

Open AI blog article: https://blog.openai.com/adversarial-example-research/

#adversarialattack #openai #novice #beginner

New attack on neural networks can alter the purpose of the neural network.

A surprising adversarial attack, whereby a perturbation to all input images can "reprogram" a poorly-defended neural network to change its task entirely. e.g. turn an ImageNet classifier into a network that counts squares.

Arxiv: https://arxiv.org/pdf/1806.11146.pdf

#Goodfellow #gbrain #adversarialattack

Most common pitfalls, you can encounter when training neural network.

http://telegra.ph/Most-common-neural-network-mistakes-07-01

#beginner #novice #dl #tutorial

Deep Mind announced that its agent beated human performance in Quake III CTF (Capture The Flag)

https://deepmind.com/blog/capture-the-flag/

#rl #quake3 #deepmind

ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations

Latest segmentation and detection approaches (DeepLabV3+, FasterRCNN) applied to street fashion images. Arxiv paper contains information about both: net and dataset.

Arxiv link: https://arxiv.org/abs/1807.01394
Paperdoll dataset: http://vision.is.tohoku.ac.jp/~kyamagu/research/paperdoll/

#segmentation #dataset #fashion #sv

Hey, our fellow colleagues at OpenDataScience community are labeling a meme dataset. You can help them with the markup just by viewing memes in this bot: @MemezoidBot

#DataSet #labeling

Neural scene representation and rendering

In June #DeepMind introduced Generative Query Network (#GQN) framework within which machines learn to perceive their surroundings by training only on data obtained by themselves as they move around scenes.

Link: https://deepmind.com/blog/neural-scene-representation-and-rendering/

#DeepMind new release: Neural Processes (#NPs) that generalise #GQN ’s training regime to other few-shot prediction tasks such as regression and classification

Arxiv 1: https://arxiv.org/abs/1807.01622
Arxiv 2: https://arxiv.org/abs/1807.01613

#ICML2018

How #Netflix used data science to make scripts for the shows:

Video: https://www.youtube.com/watch?v=qXo9jTxfqJ8&feature=youtu.be
Github: http://netflix.github.io

Really great video, showing practical approach, with some focus on human interactions and integrating data insights into the product.

#youtube #netflixresearch

Udacity has published a github repo for the Deep Reinforcement Learning Nanodegree program

Repo: https://github.com/udacity/deep-reinforcement-learning
Nanodegree: https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893

#dl #udacity #mooc #course #github #rl

Deep Learning for Matching in Search and Recommendation

PDF: http://www.comp.nus.edu.sg/~xiangnan/sigir18-deep.pdf

#sigir2018 #Tutorial

Glow by #OpenAI: Better Reversible Generative Models

Project link: https://blog.openai.com/glow/
Video: https://d4mucfpksywv.cloudfront.net/research-covers/glow/videos/both_loop_new.mp4