Marketing research almost seems to have an obsession with story telling which, perhaps, is telling. :-)

Seriously, I was at first taken aback when story telling began to get a lot of buzz because to me the word had negative connotations. (Read BS.)

Surely, there still are boring, disorganized presentations of research findings, e.g., hundreds of slides and perspiring presenters simply reading off numbers. How common this is is hard to tell.

Why does this happen? Inexperienced research execs with no time or budget to do their homework is one reason. Limited formal training in research is another. There surely are others.

Notice I've used the words why and cause. I think they are clues why some presentations fail and also keys to good storytelling (in the positive sense). We need to think causally. In fiction, events and characters are linked together causally. Even acts of nature have consequences.

Sometimes clients just want numbers or general reactions of focus group participants. At other times, though, they want us to help them better understand the why, not just the what, how, when, and so on.

Truly establishing causation is difficult to impossible even in "hard" science. But I think we often can do a better job without telling stories (in the negative sense).

Please share us if you would like

✴️ @AI_Python_EN

"OpenAI GPT-2: Understanding Language Generation through Visualization"

How the super-sized language model is able to finish your thoughts.

Blog by Jesse Vig: https://lnkd.in/ebHrTUP

#artificialintelligence #deeplearning #machinelearning

✴️ @AI_Python_EN

5 articles to learn #statistics for #datascience:

1. Comprehensive Inferential Statistics for Data Science -https://bit.ly/2NUQywr
2. Master Hypothesis Testing for Framing Data Science Problems - https://bit.ly/2u0utmV
3. Introduction to #ANOVA (with practical #Excel examples) - https://bit.ly/2F1ciE5
4. Tutorial for Understanding Non-Parametric Statistical Tests - https://bit.ly/2CcSxrr
5. Learn Statistics using R! - https://bit.ly/2VMOOIr

✴️ @AI_Python_EN

Do you remember a bullshit study published a few years ago claiming that deep learning can spot criminals from their photos and arguing that criminals have different facial features. Despite the ethical issue, we know this is bullshit but we couldn't spot the flaws.
Well, like most machine learning problems the devil is in the data.
To train the model the researchers used 700 of criminals ID photos as positive images. On other hands, they collected 1100 non-criminals from the web which featured people smiling.
No wonder why they go 90% accuracy!
So instead of developing criminals detector, they developed smiles detector LOL.
#research #machinelearning #deeplearning #ai
https://lnkd.in/fMhU4ZZ

✴️ @AI_Python_EN

Every data science professional should be on GitHub and Reddit. There are no other platforms quite like these 2 that have a pulse on the latest trends in #datascience and #machinelearning. Here are 3 articles with the best data science repositories handpicked by our team:

1. Top 5 Data Science GitHub Repositories and Reddit Discussions (February 2019) -
https://bit.ly/2GYFvBW
2. Top 5 Data Science GitHub Repositories and Reddit Discussions (January 2019) - https://bit.ly/2u0eqFT

3. 25 Best Data Science and Machine Learning GitHub Repositories from 2018 - https://bit.ly/2Up9G8h

✴️ @AI_Python_EN

Data Science vs Data Engineering



Is this still relevan?

#datascience #dataengineering

✴️ @AI_Python_EN

One of the BEST #MachineLearning Glossary by Google


It will definitely come in handy - https://lnkd.in/gNiE9JT


#machinelearing #glossaries #patternrecognition #artificialintellegence

✴️ @AI_Python_EN

#NLP is among the hottest and most interesting fields in #datascience. Check out these 5 in-depth and hands-on tutorials to learn #NLP:

1. The Essential NLP Guide to Solve Top 10 Common NLP Tasks - https://lnkd.in/fiXS5Rj
2. Practical Tutorial for Regular Expressions in #Python - https://lnkd.in/fXw-Rdz
3. A Gentle Introduction to #TopicModeling - https://lnkd.in/fDXmt4n
4. Comprehensive and Intuitive Guide to #WordEmbeddings - https://lnkd.in/fvRrFhA
5. #TextClassification using #ULMFiT and #fastai Library in Python - https://lnkd.in/f7bu8jM

And test your #NaturalLanguageProcessing knowledge on this challenging question set!
30 Questions to test a data scientist on Natural Language Processing - https://lnkd.in/fpWBZUh

✴️ @AI_Python_EN

#ReinforcementLearning is making waves - now is as good a time as any to learn what it's about. Check out these 4 articles to get started:

1. Simple Beginner’s guide to Reinforcement Learning & its implementation - https://bit.ly/2tUOPhB
2. Reinforcement Learning Guide: Solving the Multi-Armed Bandit Problem from Scratch in #Python - https://bit.ly/2NSC1kN
3. Introduction to Monte Carlo Learning using the OpenAI Gym Toolkit - https://bit.ly/2VOddx1
4. Nuts & Bolts of Reinforcement Learning: Model Based Planning using Dynamic Programming - https://bit.ly/2NOV6UY

✴️ @AI_Python_EN

How neural networks learn' - Part III: The learning dynamics behind generalization and overfitting:


https://www.youtube.com/watch?v=pFWiauHOFpY

#neuralnetwork

✴️ @AI_Python_EN

Variational Autoencoders Pursue PCA Directions (by Accident)

Rolinek et al.: https://lnkd.in/efNRnb9

#ArtificialIntelligence #DeepLearning #MachineLearning #ComputerVision #PatternRecognition

✴️ @AI_Python_EN

AI Safety Needs Social Scientists

Irving et al.: https://lnkd.in/exUPmFy

#AIEthics #AIGovernance #ArtificialIntelligence

✴️ @AI_Python_EN

Comparison of hot jobs of global #datascience analytics industry: Data Scientist vs Data Engineer vs Statistician. Download the infographic and decide the best job role for you!
https://bit.ly/2CegSgn

✴️ @AI_Python_EN

There's an art to running ML models in production the right way - and that's where a fluid DataOps plan becomes even more useful #DataScience #ODSC BluevineCapital https://hubs.ly/H0gQGkw0

✴️ @AI_Python_EN

Hacking Google reCAPTCHA v3 using Reinforcement Learning

Paper: https://lnkd.in/es9AjzC

#reinforcementlearning #research #ai #artificialintelligence #machinelearning

✴️ @AI_Python_EN

Google launches TensorFlow Lite 1.0 for mobile and embedded devices

https://venturebeat.com/2019/03/06/google-launches-tensorflow-lite-1-0-for-mobile-and-embeddable-devices/

#tensorflow #machinglearning #ai for #mobiledevices

✴️ @AI_Python_EN

Towards Structured Evaluation of Deep Neural Network Supervisors

Paper: https://lnkd.in/evfuQAq

#neuralnetworks #ai #machinelearning #artificialintelligence #deeplearning #research

✴️ @AI_Python_EN

neuralRank: Searching and ranking ANN-based model repositories

Paper: https://lnkd.in/edxKPBH
#artificialinteligence #research #machineleaning #neuralnetworks

✴️ @AI_Python_EN

So mesmerizing 😵! Python code to submit rotated images to the Cloud Vision API + R code for visualizing it. This repository was used to create this animation. Quite amazing to see what the neural network aka Google's Cloud Vision API is seeing where we or at least I needed some time to see that there is a rabbit 🐇 in the duck 🦆 or vice versa. Credits to Max Woolf for the animation. He also open-sourced the code to generate this animation. #deeplearning #machinelearning

Github: https://lnkd.in/dJ9V6tC

✴️ @AI_Python_EN

How can AI become biased? 2 papers investigate:

Joy Buolamwini et al show that AI has a higher error rate when recognizing darker-skinned female faces: http://bit.ly/2C2pxT9

IBM responds to their paper, explaining how they reduced that error: http://bit.ly/2C82u9n #TechRec #ArtificialIntelligence

✴️ @AI_Python_EN