Discover and read the best of Twitter Threads about #ml

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DAY 1: Python Installation on Windows.

Let start our 30 days journey of Twitter Python Training with:
* How to Install Python.
*Likely issues to faced after the installation of Python.

#Thread
👇👇👇
#TwitterPython #Python #ML #DL #AI #DataScience
I will like to talk about two ways of Installing and running Python on your personal computer and the method I 'prefer' out of the two.

1. Direct Installation from Python Website
2. Installation from Anaconda Website.

#TwitterPython #Python #ML #DL #AI #DataScience
1st Method:
Direct Installation from Python Website

Installing Python on your PC is not a difficult task.
It involves just a few simple steps:
a. Download binaries from python.org
b. Install the binaries
c Add Python to system environment variables
d. Install pip
Read 9 tweets
From a recent @martin_casado article posted on @a16z:

"Cloud infrastructure is a substantial – and sometimes hidden – cost for AI companies".

I'm sharing the techniques we use at @FloydHub_ to reduce this cost on #AWS and improve our gross margins [Thread] #ML #AI
0/ Not all AWS regions are priced the same. GPUs can be up to 90% more expensive across regions. Other than cost consider these when picking your AWS region: proximity to your geographical location, compliance requirements, and integration with any existing AWS infrastructure.
1/ Reserve your GPU instances and/or purchase Savings Plans. Review your GPU usage for the last 3-6 months and purchase 1-year plans based on that. Gives you 25-30% savings on your GPU bill.
Read 12 tweets
Having reviewed a bunch of radiology papers lately with the same flaws - some #ML but most not - here are my top 10 tips! #AcademicChatter #radiology #AI
1/ Start with what is already known, where is the gap in knowledge and why it's important. Some papers are doomed from the start because there is no clear impact of the work. Remember the reviewer may not be super-specialised in your research niche.
2/ I need to understand the experimental design quickly, so a flowchart is really helpful. Put yourself in the shoes of a first-time reader. If the design is easy to follow then everything else fits into place.
Read 11 tweets
1/13 Hoje irei falar para vocês sobre dois conceitos que são menos independentes do que parecem para alguns: Causalidade e predição. Confesso que é até estranho tratá-las como duas coisas separadas, ou diferentes, e espero convencê-los ao... #IA #AI #ML #causality #bookofwhy
2/13 final dessa thread de que essa visão é fundamentada. Na década de 50, Jacob Yerushalmy realizou um estudo onde acompanhou 15 mil crianças da região da baía de São Francisco. Para surpresa de Yerushalmy, e contrariando o que já se mostrava forte na época (que fumar
3/13 fazia mal a saúde), seus resultados indicavam que bebês de mães fumantes nascidos com baixo peso tinham mais chances de sobreviver do que bebês de mães não fumantes nascidos com baixo peso. Não era um estudo de inferência causal, era apenas predição, alguns podem dizer.
Read 14 tweets
How To Train Interpretable Neural Networks That Accurately Extrapolate From Small Data. Today we released a new paper that showcases how to do just that using Scientific Machine Learning (#sciml) techniques to encode non-data scientific information.

stochasticlifestyle.com/how-to-train-i…
Our approach builds upon the work of @DavidDuvenaud but identifies that the differential equations one works with does not have to be a blackbox, but instead can utilize all of the available scientific models to encode as much prior information as possible.
@DavidDuvenaud This is a structure that we call the Universal Differential Equation: a differential equation with embedded universal approximators. Sometimes neural networks, sometimes Chebyshev polynomials, for us it really doesn't matter because they are small approximators.
Read 12 tweets
Starting 2020 off right by getting the benefit of hindsight & reading some classic papers.

#epielliereads
1st up, Take the Con out of Econometrics by Edward Leamer, 1983: all about pros & cons of randomized & observational studies plus answers to many of recurrent #econtwitter, #statstwitter, & #epitwitter arguments & even why #ML can’t do causal inference!
jstor.org/stable/1803924
In Part I randomized experiments vs natural experiments, aka observational studies, Leamer provides a nice summary of some of the problems that can arise in RCTs including that randomization guarantees validity *on average* but that chance imbalance can make any given RCT biased
Read 13 tweets
#AI #DevOps #Hackathon reminder. The final mile in any applied #ML project is deployment into the hands of the people that need it. This is an engineering problem requiring a specific set of skills which this hackathon will test. 1-4 people/team. Is your team ready for this? 1/n
Interested? Send a structured email to DevOpsAiChallenge@retina-ai.com with header "AI DevOps." Email should contain:

1) Team name
2) Names & emails (cc) of all team members
3) Relevant skills & experience of team members, with link to Github if available. & member role(s) 2/n
4) Brief description of the specific pre-qualifying exercise you intend to submit.

A pre-qualification exercise is due by Jan 20. Based on that, qualifying teams will be selected and announced by Jan 22, 2020. 3/n
Read 6 tweets
The German Army (Amt für Heeresentwicklung, to be exact) has surprised many and published a position paper on #AI for land-based forces. Will translate bits and comment.
Alright, let's do this!
The paper is available here 👇
It was published only some ten days ago, but was finished in August/September.
#AI #KI #Bundeswehr (1/)
augengeradeaus.net/wp-content/upl…
The first thing to note is that with regard to process, this paper is a bit weird: Germany has a national #AI strategy (which mentions military AI in *one sentence*) but no military #AI strategy for the whole of the Bundeswehr. However, now there is this paper for the Army.
(2/)
Read 33 tweets
1/ "Wait, what?" tweetstorm
2/ Wow, #ML powered upsampling has gotten very good. Example video, VGA input, 4K output, from 1990s anime called Yu Yu Hakusho:
3/ To compare, this is a sample of the original, though not the exact one used as input:
Read 6 tweets
We've evolved and here is a #thread of that journey!

Like every successful living species, to survive and get better, you have to evolve. & a company is not different. With the big tech eating into almost every industry it becomes paramount important that traditional businesses
have to think ground up with digital technologies at the core. And today, I'm here to announce that, as a business leader of your company, you don't have to do it alone - Heptagon is there to guide you through your entire digital transformation journey.
We've been creating digitally transformative solutions since 2009. We had an opportunity to work with the NYC government, BMW and we've also built platforms to distribute these solutions. These are our success stories but, we've had our fair share of failures
Read 11 tweets
Alors les amis, The French Ministry of Armed Forces #AI task force has released a new report covering #AI in #defence & a roadmap toward more military adoption. You can read it en français here: defense.gouv.fr/salle-de-press…

Or read on for a few findings & preliminary thoughts.
FR wants its very own JAIC-like cell, the CCIAD, embedded within their Defence Innovation Agency (IAD). (Shout out: FR speakers should follow @KrajeckiMichael). The Coordination Cell for AI in Defence will facilitate efforts on coordination & strategy. p21 for this buried lede
Nice to see that France does not see AI as an end unto itself. Intro is pragmatic & focusses on the weaknesses of second-wave AI systems (although without using the #DARPA-esque vocab), including by noting that #AI does not change the nature of war.
Read 18 tweets
Automation Tools To Ease Your Data Science Project

Working on Data Science Project can be overwhelming for beginners esp student that intend to carry our a research in this field.

Below are some automation tools that data science professional can use.
#DataScience #Thread
1 WEKA
Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization
#WEKA help you discover practical data mining &learn to mine your own data
#WEKA make it easy for you to play around with data and it gives you test options regarding how you split your #dataset.
I once used WEKA to get result 'clue' for Classification of EEG signal using AIRS, Immuno, CLONALG.

Download WEKA: cs.waikato.ac.nz/ml/weka/
Read 11 tweets
Love the simplicity of #Serverless #FaaS (Function-as-a-Service) but hate the setup process? Look to these 7 open source projects to ease #AWS Lambda deployments: dy.si/M3VJd
———
#BigData #StreamingAnalytics #Cloud #DataScience #MachineLearning #IFTTT #EventDriven #AI
13 free tools for #API design, development, & testing — for example, Amazon API Gateway allows you to build front-end APIs for applications built on Amazon EC2, #AWS Lambda, or any web application:
dy.si/1VTf942
————
#microservices #cloud #serverless #FaaS #coding #IoT
#IFTTT alternatives for developers of #EventDriven workflows: dy.si/ZorG2N
————
#IoT #EdgeAnalytics #EdgeComputing #Microservices #DataScience #BigData #FaaS #ML
————
+See the book “AWS Lambda in Action: Event-driven #Serverless Applications” at amzn.to/2YFDSO8
Read 4 tweets
Department of Medicine Grand Rounds - listening to the amazing patient experience transformation journey led by Babar Hasan and his stellar team (including Muneera Rasheed, Zahra Hoodbhoy, @mominkazi) @AKUGlobal
#mentorship a key component of delivering #compassionate care by nursing - Noreen Sultan @AKUGlobal
#patient experience #transformation using predictive modeling based on #ML - leading with #data @Ephlux @AKUGlobal
Read 3 tweets
A "worrying analysis":

"18 [#deeplearning] algorithms ... presented at top-level research conferences ... Only 7 of them could be reproduced w/ reasonable effort ... 6 of them can often be outperformed w/ comparably simple heuristic methods."

Paper:
lnkd.in/dTaGCTv

#AI
[Updates worth tweeting]

2/
There is much concern about #reproducibility issues and flawed scientific practices in the #ML community in particular & #academia in general.

Both the issues and the concerns are not new.

Isn't it time to put an end to them?
3/
There are several works that have exposed these and similar problems along the years.

👏👏 again to @Maurizio_fd et al. for sharing their paper and addressing #DL algorithms for recommended systems (1st tweet from this thread).

But there is more, unfortunately:
Read 18 tweets
Remember the #BachDoodle? We’re excited to release paper on Behind-the-Scenes design, #ML, scaling it up, and dataset of 21.6M melodies from around the world!
📜 arxiv.org/abs/1907.06637
w/ @fjord41 @ada_rob @notwaldorf @bengiswex Leon Hong @jaxcooo
tl; dr
1/ In three days, people spent 350 years worth of time playing with the Bach Doodle, and the “harmonize” button was clicked more than 55 million times.
2/ The model Coconet 🥥 is an instance of OrderlessNADE and uses Gibbs sampling to generate the harmonizations through rewriting.
📜 Previous blogpost: g.co/magenta/coconet
📝 Paper from #ISMIR 2017: arxiv.org/abs/1903.07227
Read 9 tweets
Recently, the National Crime Records Bureau of India published a tender for the Automated Facial Recognition System (AFRS). Some background (B), and key takeaways (T). #AI #FacialRecognition #ML #India 1/n
B1. The AFRS is conceptualised to modernize the police force, for “criminal identification, verification and its dissemination among various police organizations and units across the country.” 2/n
B2. The rationale: face recognition will help with “facilitating easy recording, analysis, retrieval and sharing of Information” and help in identification of: criminals, missing children/persons, unidentified dead bodies and unknown traced children/persons. 3/n
Read 17 tweets
I’ve been getting a lot of new followers lately, so how about an #epitwitter #ScholarSunday so newcomers can get connected to our lovely community!

Here’s a thread of some of my suggestions for who to follow & why! Add your own in the comments👇🏼
Looking for some academic career advice? Have questions about writing grants? Want tips on making delicious pizza or pie? Head on over to @BillMiller_Epi!
Want support for how to #liveyourbestlife, learn to say ‘no’, or throw an amazing party? You’ll be wanting to follow @lisabodnar!
Read 6 tweets
#STPIINDIA has been playing the role catalyst in bolstering the growth of #IT industry by providing state-of-the-art incubation facilities to #startups,#MSMEs & budding entrepreneurs in IT/ITeS sector to nurture tech-fueled entrepreneurship in India.#STPIIncubation #KnowAboutSTPI
In the early 90’s, when development of physical infrastructure needed massive investment, #STPIINDIA pioneered incubation services to nurture budding IT companies & promote them to leverage the enormous opportunities in software exports. #STPIIncubation #KnowAboutSTPI @Omkar_Raii
#STPIINDIA has been providing world-class incubation facilities to #startups, #MSMEs & budding entrepreneurs in #IT industry since 1991 to stimulate the growth of tech entrepreneurship pan-India by enabling hassle-free operations. #STPIIncubation #KnowAboutSTPI @Omkar_Raii
Read 48 tweets
Happy 4th of July!!
One area of intergration of ML and econometrics is providing inference after variable selection (Post selection Inference) #rstats #econtwitter #Rladies #ML #econometrics 1/n
Most popular technique in economics is the 'Double LASSO' which provides inference on the treatment effect after variable selection using LASSO. Check out the R package 'hdm'. cran.r-project.org/web/packages/h…
#rstats #econtwitter 2/n
We are often interested in conducting inference on other selected covariates (controls) as well. Check out R package 'selectiveInference' which conducts inference on multiple covariates. cran.r-project.org/web/packages/s…
#rstats #econtwitter 3/n
Read 5 tweets
"If I were to write a textbook on #complexity, it would be structured like this..."
- SFI Pres David Krakauer starts off this morning at #CSSS19 #CSSS at @IAIASantaFe

#error #adaptation #universality #coarsegrained #math #theory
"If you talk about #intelligence and #stupidity, you talk about #communication and #policy. That is the natural place to start."

"If you've ever written a Lokta-Volterra #equation, you've drawn a perpetual motion machine. There's no #dissipation."

- SFI President David Krakauer
"Why #God is a bad #theory: it's not because it's not true; it's because it uses an infinite-dimensional process to explain low-dimensional phenomena."

- SFI President David Krakauer at #CSSS19 on #Ockham's Razor & why #Darwin's animal breeding metaphor backfired on #evolution
Read 7 tweets
Why Python?

A thread as requested by @hackSultan

Will try to make this as unbiased as possible. 😁

👇🏾
Python is a high-level programming language whose structure mimics the way humans think.
It abstracts away low-level stuff like memory management, pointers etc.. which are the main stay of other languages like C++, Java
It is dynamically typed. Don’t be perturbed. Basically when u say a language is statically typed, it means you need to declare your variables upfront. You need to tell the compiler how much memory to set aside to handle a variable in advance.
Read 24 tweets
With the new @Tesla software update + browser, @c9r can view @swim's traffic.swim.ai app in his Model 3 as he drives through Palo Alto, CA. Live #ML predictions about untimed traffic signals streaming from city traffic infrastructure. Check it out @elonmusk! #swimOS
1/ If this is your first time seeing the @swim traffic.swim.ai app, here's what you're looking at...
2/ The app displays a map of Palo Alto, CA. We use @Mapbox here, but the UI core is all @swim.
Read 19 tweets
#Bigdata vs Machine Learning vs Artificial Intelligence
By Irene Aldridge
☝️author High-Frequency Trading: A Practical Guide to #Algorithmic Strategies& #Trading Systems
☝️co-author Real-Time Risk: What Investors Should Know About #Fintech,#highfrequencytrading and FlashCrashes
1. ☝️🧐➿🔢 In traditional #statistics or #econometrics, researchers make assumptions about #data distributions ahead of the analysis
2. 🥇💪#machinelearning = the 1st discipline to apply #efficiency to #problemsolving brought by #computers & their enhanced computational power
3. 🤗➿🔢#ML scientists try to reduce #assumptions about the data as much as possible&let the data (&computers) decide what fits best.
4. ♾➡️🔤🌐#Datascience identifies core characteristics of the data, summarized by what has been known as #characteristic values (#eigenvalues)
Read 6 tweets

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