Discover and read the best of Twitter Threads about #MachineLearning
Most recents (14)
#Thread on new features and updates announced in release notes of #MSDyn365 Oct'18: docs.microsoft.com/en-au/business…. This is an excellent longread. Thread is just #TLDR for #CE specific/related features
#PowerApps #CDS #Flow #USD #Sales #FieldService #USD #Portals #AzureML
#PowerApps #CDS #Flow #USD #Sales #FieldService #USD #Portals #AzureML
Starting with #MSDyn365 #Sales updates:
1. Playbooks: Think of it as Barney Stinson's playbook. Basically set of "automated repeatable sales activities" that help in winning opportunities. Looks like it'll be a set of activities which could be assigned to users
1. Playbooks: Think of it as Barney Stinson's playbook. Basically set of "automated repeatable sales activities" that help in winning opportunities. Looks like it'll be a set of activities which could be assigned to users
2. #MSDyn365 #Sales will feature deeper integration with LinkedIn, including capability to send InMail and adding LinkedIn related step in Business Process Flows
3. Out of the box @MicrosoftTeams integration
3. Out of the box @MicrosoftTeams integration
We are currently in the keynote. The speaker is talking about how popular Neo4j is
Keynote now telling us about all the new Neo4j features. Examples: location filter, including 3d. Auto cache reheating. I'm interested to know if auto cache reheating is working with query patterns or meant to replace them or what
Bayes’ Theorem Definitions:
The vertical bar | stands for "given that".
P = Probability.
A & B are events.
P(A) & P(B) are the probabilities of events A and B. Each event is separate from the other.
P(A|B) is the probability of A being true given that event B is true.
#SoDS18 #ML
The vertical bar | stands for "given that".
P = Probability.
A & B are events.
P(A) & P(B) are the probabilities of events A and B. Each event is separate from the other.
P(A|B) is the probability of A being true given that event B is true.
#SoDS18 #ML
Example:
Say we have 2 coolers at an owambe: Cooler A is filled with 10packs of small chops only. Cooler B has 5packs of small chops and 5packs of Asun. You are then asked to close your eyes and pick a pack out of one cooler, which pack would you pick? #MachineLearning #SoDS18
Say we have 2 coolers at an owambe: Cooler A is filled with 10packs of small chops only. Cooler B has 5packs of small chops and 5packs of Asun. You are then asked to close your eyes and pick a pack out of one cooler, which pack would you pick? #MachineLearning #SoDS18
Because you know that we have more of small chops in both coolers, your brain is most likely going to tell you have picked a pack of small chops - even when your eyes are closed. This is not wrong.
#MachineLearning #SoDS18
#MachineLearning #SoDS18
Wer das Thema Künstliche Intelligenz #KI und #MachineLearnning im Unterricht behandeln möchte, findet in diesem Thread ein paar Beispiele, interaktive Spiele und Experimente 🤓🤖👇
#digitaleBildung #schule #twitterlehrerzimmer #unterricht
#digitaleBildung #schule #twitterlehrerzimmer #unterricht
Künstliche Intelligenz wird zum Beispiel bei der automatischen #Bilderkennung eingesetzte. Auf thing-translator.appspot.com kann man Gegenstände in die Kamera halten, ein Foto machen und die #KI interpretiert die Bilddaten 📸 🤓
Auch unter cloud.google.com/vision/ können Bilder hochgeladen werden - hier werden sogar noch mehr Informationen angezeigt und einzelne Objekte und Gesichter in den Bildern erkannt (Bildanalyse von Google) 👇🤖 #KI
@francesc @vadimlearning First #MLonCode paper presented by @vadimlearning is "code2vec: Learning Distributed Representations of Code" by Uri Alon, Meital Zilberstein, @omerlevy_, and @yahave.
arxiv.org/abs/1803.09473
arxiv.org/abs/1803.09473
@francesc @vadimlearning @omerlevy_ @yahave The second #MLonCode paper, also presented by @vadimlearning, is "A General Path-Based Representation for Predicting Program Properties" by ... wait for it ... Uri Alon, Meital Zilberstein, @omerlevy_, and @yahave 🎉
arxiv.org/abs/1803.09544
arxiv.org/abs/1803.09544
@francesc @vadimlearning @omerlevy_ @yahave Next, our #ML intern Romain Keramitas, is reviewing the #MLonCode paper "Mining Idioms from Source Code" by Miltiadis Allamanis and @RandomlyWalking
arxiv.org/abs/1404.0417
arxiv.org/abs/1404.0417
How many random seeds are needed to compare #DeepRL algorithms?
Our new tutorial to address this key issue of #reproducibility in #reinforcementlearning
PDF: arxiv.org/pdf/1806.08295…
Code: github.com/flowersteam/rl…
Blog: openlab-flowers.inria.fr/t/how-many-ran…
#machinelearning #neuralnetworks
Our new tutorial to address this key issue of #reproducibility in #reinforcementlearning
PDF: arxiv.org/pdf/1806.08295…
Code: github.com/flowersteam/rl…
Blog: openlab-flowers.inria.fr/t/how-many-ran…
#machinelearning #neuralnetworks
Algo1 and Algo2 are two famous #DeepRL algorithms, here tested
on the Half-Cheetah #opengym benchmark.
Many papers in the litterature compare using 4-5 random seeds,
like on this graph which suggests that Algo1 is best.
Is this really the case?
on the Half-Cheetah #opengym benchmark.
Many papers in the litterature compare using 4-5 random seeds,
like on this graph which suggests that Algo1 is best.
Is this really the case?
However, more robust statistical tests show there are no differences.
For a very good reason: Algo1 and Algo2 are both the same @openAI baseline
implementation of DDPG, same parameters!
This is what is called a "Type I error" in statistics.
For a very good reason: Algo1 and Algo2 are both the same @openAI baseline
implementation of DDPG, same parameters!
This is what is called a "Type I error" in statistics.
Today we're at #FOSD, talking about the future of software development with influential individuals in the fields of #MLonCode, #QuantumComputing, and #blockchain technologies.
Follow this thread for live tweeting!
Follow this thread for live tweeting!
What #machinelearning models are used at hedge funds and other investment firms? Audience (200 ppl) poll at @Bloomberg ML in Finance event hosted by @jbaksht
And in what areas...
And in what asset classes.
More hype around AI, mHealth, wearables. @AliveCor devices can use #AI to find LQTS:
prnewswire.com/news-releases/…
prnewswire.com/news-releases/…
From the press release:
AUC 0.83, specificity 81%, sensitivity 73%, accuracy 79%
Prevalence of LQTS 160,000 out of US population of 325.7 million (0.049%)
AUC 0.83, specificity 81%, sensitivity 73%, accuracy 79%
Prevalence of LQTS 160,000 out of US population of 325.7 million (0.049%)
Do we need to do a tweetatorial about how this is grossly insufficient accuracy and will almost certainly lead to more harm than good?
(1/5) I grew up in the Programming Languages research community and have recently begun attending Machine Learning conferences. One perspective that I don't see much in either community is that #MachineLearning is a form of #programming.
(2/5) PL/formal methods researchers tend to think of programs as engineered objects, and study abstractions/tools for principled engineering. But the big assumption here is that you can formalize your goals and the world in which your programs run. That's not always realistic.
(3/5) In contrast, #MachineLearning lets programs be "found" objects. "I don't have a full spec for my program and can't write the code myself, but here's some data on what it does. Discover it!" This is still #programming, albeit done inductively rather than deductively.
@msharmas @HIMSSIndia @supten @bipin4uk @55bio @ManishreeBhatt1 @krayker @rmnth @drvikram @kousikraj @PhilipsBlore @drruchibhatt @JyotiSahai @oommen_john @DrUjjwalRao @rbatra868 @edrneelesh @milindantani @iArnabPaul @mTatva @ashwinnaik @DrJagdishChatur @pankajguptadr @sbbhattacharyya Stay tuned to the #HIMSSIndia Blog Carnival with insights shared by the experts during the @HIMSSIndia Conference and Exhibition 2018
1: Conference Information and links- blog.hcitexpert.com/2018/04/himss-…
2: How to bridge Healthcare's Digital Divide @Ishaq_Quadri blog.hcitexpert.com/2018/04/how-to…
1: Conference Information and links- blog.hcitexpert.com/2018/04/himss-…
2: How to bridge Healthcare's Digital Divide @Ishaq_Quadri blog.hcitexpert.com/2018/04/how-to…
@msharmas @HIMSSIndia @supten @bipin4uk @55bio @ManishreeBhatt1 @krayker @rmnth @drvikram @kousikraj @PhilipsBlore @drruchibhatt @JyotiSahai @oommen_john @DrUjjwalRao @rbatra868 @edrneelesh @milindantani @iArnabPaul @mTatva @ashwinnaik @DrJagdishChatur @pankajguptadr @sbbhattacharyya @Ishaq_Quadri 3. #HIMSS18: Where the brightest minds in healthcare meet !! by @tejasvdeshmukh
blog.hcitexpert.com/2018/04/himss2…
#HIMSSIndia #HITsmIND
blog.hcitexpert.com/2018/04/himss2…
#HIMSSIndia #HITsmIND
@msharmas @HIMSSIndia @supten @bipin4uk @55bio @ManishreeBhatt1 @krayker @rmnth @drvikram @kousikraj @PhilipsBlore @drruchibhatt @JyotiSahai @oommen_john @DrUjjwalRao @rbatra868 @edrneelesh @milindantani @iArnabPaul @mTatva @ashwinnaik @DrJagdishChatur @pankajguptadr @sbbhattacharyya @Ishaq_Quadri @tejasvdeshmukh 4. Vision 2025: What Health Care Could Look Like a Decade from Now by Dr. Vicky Parikh, @ParikhVicky blog.hcitexpert.com/2018/04/vision…
#HIMSSIndia #HITsmIND
#HIMSSIndia #HITsmIND
1/6 Data and the Quest for Truth: A short cartoon talking about the value of data in establishing truth. First, distinguish between matters of opinion and matters of fact.
#datascience #scientificmethod #machinelearning
#think2018 #IBMPartner @ibmanalytics
#datascience #scientificmethod #machinelearning
#think2018 #IBMPartner @ibmanalytics
2/6 You can have a preference and that’s cool. You like one thing. I like another. We run into problems when we argue about verifiable facts. Watch out for #confirmationbias
#datascience #analytics #confirmationbias
#think2018 #IBMPartner
#datascience #analytics #confirmationbias
#think2018 #IBMPartner
3/6 Ignoring evidence can impact people around you… and that’s bad. We live in a #BigData in which everything is quantified. To solve our problems, we need to become data-literate.
#datascience #machinelearning
#think2018 #IBMPartner
#datascience #machinelearning
#think2018 #IBMPartner
Inspired by the big ol' long list of deep learning models I saw this morning, and @SpaceWhaleRider's love of science-y A-Z lists, I've decided to create an A to Z series of tweets on popular #MachineLearning and #DeepLearning methods / algorithms.
Ready? Here we go:
Ready? Here we go:
A is for... the Apriori Algorithm!
Intended to mine frequent itemsets for Boolean association rules (like market basket analysis). Ex: if someone purchases the same products as you, in general, then you'd probably purchase something they've purchased.
cran.r-project.org/web/packages/a…
Intended to mine frequent itemsets for Boolean association rules (like market basket analysis). Ex: if someone purchases the same products as you, in general, then you'd probably purchase something they've purchased.
cran.r-project.org/web/packages/a…
B is for... Bootstrapped Aggregation (Bagging)!
This is an ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification+regression. Reduces variance, helps to avoid overfitting.
Example: Random Forests.
This is an ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification+regression. Reduces variance, helps to avoid overfitting.
Example: Random Forests.
1/ A new episode of #Perspectives! How will automation impact the industry, and you personally, over the next twelve months? perspectives.voxpopme.com/episode/episod… 
2/ How will automation impact the industry and @dbrowell? perspectives.voxpopme.com/episode/episod… #mrx 
3/ How will automation impact the industry and @andybuckers? perspectives.voxpopme.com/episode/episod… #newmr 
