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✴️ @AI_Python_EN

Our Daily ArXiv Channel:
🗣 @AI_Python_Arxiv

BTW: Thank You For Joining :)

Pedro A. Ortega:

Very excited about our new DeepMindAI tech report with @janexwang and colleagues! Memory-based meta-learning leads to Bayes-optimal sequential prediction strategies - the memory tracks the sufficient statistics. See here:
https://arxiv.org/abs/1905.03030

✴️ @AI_Python_EN

Check out our new #GAN work on translating images to unseen domains in the test time with few example images.

Live demo
http://bit.ly/2LyW4Y3

Project page
http://bit.ly/2HbcRLf

Paper
http://bit.ly/2Ly3VVX

Video
http://bit.ly/2Va86a3
#NVIDIA

✴️ @AI_Python_EN

Walkthrough - When and How to Use MLP, CNN, and RNN Neural Networks - Jason Brownlee

To follow posts: https://lnkd.in/ev9S2hh

#machinelearning #artificialintelligence #datascience #ml #ai #deeplearning #MLP #CNN #RNN #neuralnetworks

✴️ @AI_Python_EN

Deep Learning course: lecture slides and lab notebooks

Built and maintained by Olivier Grisel and Charles Ollion: https://m2dsupsdlclass.github.io/lectures-labs/

#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks #MachineLearning

✴️ @AI_Python_EN

6 Fun Machine Learning Projects for Beginners If you want to master machine learning, fun projects are the best investment of your time.

🌎 Machine Learning Projects

#artificialintelligence #machinelearning

✴️ @AI_Python_EN

In honor of the last day of ICLR 2019, here are the two papers that won Best Paper Award this year:

MILA’s “Ordered Neurons :

https://arxiv.org/abs/1810.09536


MIT CSAIL’s “The Lottery Ticket Hypothesis :

https://arxiv.org/abs/1803.03635

✴️ @AI_Python_EN

Many methods used in #marketing_research (MR) in the 21st century have been adapted from methods developed in medical research and epidemiology in the last century.

They aim to identify both causes and cures, as we do in MR. Like #MR, they utilize experimental, quasi-experimental and observational data.

Here are some books on these subjects I've found helpful:

🔸 - Fundamentals of Clinical Trials (Friedman et al.)
🔸 - Introduction to Statistical Methods for Clinical Trials (Cook and DeMets) 
🔸 - Case-Control Studies (Keogh and Cox)
🔸 - Handbook of Statistical Methods for Case-Control Studies (Borgan et al.)
🔸 - Modern Epidemiology (Rothman et al.)
🔸 - Epidemiology: Study Design and Data Analysis (Woodward)
🔸 - Spatio-Temporal Methods in Environmental Epidemiology (Shaddick and Zidek)
🔸 - Handbook of Spatial Epidemiology (Lawson et al.)**

#DataScience has been rightly criticized for mining data for correlations that prove ephemeral ("Torture the data until it confesses. Then the data recants its confession.")

However, in fairness, complete understanding of causation is not necessary to act, otherwise humans would have vanished long ago.

There are many medical conditions we do not fully understand that we can treat effectively.

Researchers need to be both rigorous and realistic.

#Statisticians often walk a sort of tightrope with rigor on one side and reality on the other. Our clients typically want quick yes-or-no answers but we need to be careful how we phrase our explanations lest we slip and fall.

Some clients are quite curious, however, and see interactions with statisticians and methodologists as an opportunity to learn. They may even have read popular books on statistical topics by Nate Silver, Philip Tetlock or other authors.

The danger there is that these books are not completely non-technical and important points can be missed or misunderstood. (Daniel Kahneman comes to mind too, and some marketing researchers seems to have misconstrued what he'd actually written in "Thinking, Fast and Slow.")

✴️ @AI_Python_EN

Interesting paper with a simple and straightforward explanation about NN pruning, based on the following hypothesis:

"Dense, randomly-initialized, feed-forward networks contain subnetworks that - when trained in isolation - reach test accuracy comparable to the original network in a similar number of iterations."

#machinelearning #deeplearning #neuralnetwork #NN

✴️ @AI_Python_EN

Best Practices for Preparing and Augmenting Image Data for #ConvolutionalNeuralNetwork s
#CNN
🌎 Best Practices

✴️ @AI_Python_EN

Yann LeCun

Video, slides and paper of my keynote at ISSCC 2019, in which I speculate on the past, present and future of #DeepLearning hardware.

🌎 Video

🌎 Slides

🌎 ISSCC 2019

✴️ @AI_Python_EN

Proffessor Rima Alaifari

New openings in my group ETH Zürich: #PhD and #Postdoc positions in nonlinear inverse problems/applied harmonic analysis and in #MachineLearning . Prospective candidates should please send their CV and accompanying documents to me by email.

✴️ @AI_Python_EN

****Face Detection With Python****

Credits - Kristijan Ivancic

Link - https://lnkd.in/fyUXKSa

#facedetection #python #computervision

✴️ @AI_Python_EN

**** How to Stand Out in a Python Coding Interview ****

Link - https://lnkd.in/fz6r7_h

Credits - James Timmins

#pythonprogramming #python #pythonprogramminglanguage

✴️ @AI_Python_EN

Google Machine Learning (101 slides) - Jason Mayes

posts: https://lnkd.in/ev9S2hh

#machinelearning #artificialintelligence #datascience #ml #ai #DeepLearning #BigData #NeuralNetworks #Algorithms #DataScientists #ReinforcementLearning

✴️ @AI_Python_EN

Mathematics for Machine Learning

Credits - Garrett Thomas

#data #datascience #machinelearning #deeplearning #dataanalytics #dataanalysis

✴️ @AI_Python_EN

PointNetLK: Robust & Efficient Point Cloud Registration using PointNet. The paper describes how to perform alignment using deep learning on point sets — Lucas-Kanade style! Alignment videos (vs. ICP) look sweet!
https://youtu.be/J2ClR5OZuLc
https://arxiv.org/abs/1903.05711
#ComputerVision

✴️ @AI_Python_EN

Notes for iclr2019 available here:

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

#ICLR2019

✴️ @AI_Python_EN

"Why #NeuralNet s generalize" Love the Q&A-style intro
Full interactive version:
http://guillefix.me/nnbias/

✴️ @AI_Python_EN

Want to turn your #self_supervised method into a #semi_supervised learning technique? Check out
(https://arxiv.org/abs/1905.03670 )

#MachineLearning #DeepLearning #artificialintelligence

✴️ @AI_Python_EN