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

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Hacking Google reCAPTCHA v3 using Reinforcement Learning

Paper: https://lnkd.in/es9AjzC

#reinforcementlearning #research #ai #artificialintelligence #machinelearning

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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

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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

OpenAI has created activation atlases (in collaboration with Google researchers), a new technique for visualizing what interactions between neurons can represent.

As AI systems are deployed in increasingly sensitive contexts, having a better understanding of their internal decision-making processes will let us identify weaknesses and investigate failures.

Blog: https://lnkd.in/d4i6xQC
Paper: https://lnkd.in/dGNcd4K
Github: https://lnkd.in/d-2WhfN
Demo: https://lnkd.in/dBiHZv3

#deeplearning #research

✴️ @AI_Python_EN

New NLP News: GPT-2, Sequence generation in arbitrary order, and much more http://newsletter.ruder.io/archive/160799

✴️ @AI_Python_EN

Deep Learning for Science School

July 15 - 19th, 2019

Lawrence Berkeley National Laboratory, Berkeley, CA

Hosted by Computing Sciences at Berkeley Lab: https://dl4sci-school.lbl.gov/

#artificialintelligence #deeplearning #sciences

✴️ @AI_Python_EN

Full-stack data science and engineering will prevent innovation

Regardless of the size of your organisation, if you want to use data science to innovate for your industrial output, don't push for a full-stack data scientist. Prevention of specialising will kill innovations. Data science means R&D work at the core and data and ML engineering requires a different focus. The collaboration of course needed but one-fit-all is a doomed strategy for innovation.

#datascience #ml #datascienceisresearch

✴️ @AI_Python_EN

Real-Time AR Self-Expression with Machine Learning #DataScience #MachineLearning #ArtificialInteligence http://bit.ly/2tYa7Lh

✴️ @AI_Python_EN

This part only provides a quick glance at some important features in Python 3. If you're interested in all of the most important features, please read the official document, What’s New in #Python .

Github Link - https://lnkd.in/ftcp5jQ

#python #datascience #machinelearning #dataanalysis

✴️ @AI_Python_EN

Amazon Comprehend Medical – Natural Language Processing for Healthcare Customers | Amazon Web Services https://amzn.to/2QJLS0W #AI #DeepLearning #MachineLearning #DataScience

✴️ @AI_Python_EN

No matter how great we think the AI, ML, DL algorithms we created are, nothing beats a human-made being!

The future lies in cobots with human-made intelligence running the world and AI as a tool.

Make friends, build a family, use AI as a tool to make your life better at work and turn it off when you can!

#algorithms #ai #machinelearning

✴️ @AI_Python_EN

Bio-LSTM: A Biomechanically Inspired Recurrent Neural Network for 3D Pedestrian Pose and Gait Prediction #DataScience #MachineLearning #ArtificialIntelligence http://bit.ly/2XPR979

✴️ @AI_Python_EN

Useful post to generate images by means a Generative Adversarial Networks (#GANs).

This is an unsupervised learning problem combining game theory and #ReinforcementLearning.

You will learn in the post from basics of GANs to implementation of the model in #TensorFlow.

Post: https://lnkd.in/dcRJp-8

Github: https://lnkd.in/d2yu-t9

If You Like Our Channel,invite your friends and share it

✴️ @AI_Python_EN

This article summarizes and explains some of the most frequently used algorithms in NLP
https://medium.com/@ODSC/essential-nlp-tools-code-and-tips-39b7b2b7d7ba

✴️ @AI_Python_EN

Step 1: pip install ludwig
Step 2: Download a csv dataset
Step 3: Create a model definition yaml file to specify input and output features
Step 4: Run ludwig experiment --data_csv path_to_csv --model_definition_file model_definition.yaml
Step 5: Receive a high accuracy model for rating a clothing item from a Kaggle dataset or any other dataset
Step 6: WOW! This is almost like making noodles in 2 minutes!

A few years ago, when I helped establish a new HP office in Braunschweig, Germany for a newly acquired team, it was a building located on a street called Ludwig Strasse and my German GPS confused me so much that I wished I had a self driving German car to locate this building :) BTW, almost every other street in Germany is named Ludwig something, right Simon Winkelbach ? :)

Curiosly enough, Uber names its self-driving deep learning model design framework Ludwig and I am immediately reminded of LudwigStrasse in Braunschweig. I decided to give this Uber Ludwig a self-driving spin and it reminded me of Microsoft AzureML studio (which is a more visual design framework of course)
https://lnkd.in/gijwygv

#deeplearning
#kaggle

✴️ @AI_Python_EN

Very nice blog post by John Langford on code submission policy https://lnkd.in/exAi6Cw. I agree with many of John's points. This is pretty much what Kamalika Chaudhuri and I are trying to accomplish this year at ICML by introducing optional supplementary code submission: https://lnkd.in/eFfTTQK


✴️ @AI_Python_EN