The biggest enemy of content creators:
Writer's block.
Some people spend *months* developing their knowledge — when it is time to write, it is gone!
Here are 5 types of writer's block and how to solve them.
A Library for Representing Python Programs as Graphs for Machine Learning
https://t.co/0Evj5cLXNZ
Graph representations of programs are commonly a central element of machine learning for code research. We introduce...
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⚡️Preprint: Why do tree-based models still outperform deep learning on tabular data?
We give solid evidence that, on tabular data, achieving good prediction is easier with tree methods than deep learning (even modern architectures) and explore why
https://t.co/qft4LgZm0z
1/9
A python module to fetch relevant papers based on keywords from different sources, including Arxiv, ACL, ACM, PMLR, CVF etc. and fetch all citations of a research paper from google scholar https://t.co/SdK1HiayTC
A Friday ML use case📕
Linkedin uses ML to help their sales representatives segment, prioritize, and help target accounts.
To make recommendations useful, they added user-facing explainability features that provide narrative insights.
How it works 👇🏼
https://t.co/qlXyyvRaTY
@richardtomsett @alt227Joydeep@haltakov Exactly, the curve can give some insights and AUC add to that. This post mentions a good article comparing AUC-ROC and AUC-PR https://t.co/wYhvnMGrna. Imbalance data is always tricky and better look at different metrics --- even comparing distribution between train & test sets
@alt227Joydeep@haltakov Oh you shouldn't trust AUC-ROC for imbalance data, same as for Accuracy. You are best looking at AUC-PR (area under the precision recall curve) or MCC (Matthews correlation coefficient)
Very interesting analogy between fast (AI) decisions and fast food. You can always cook at home or get it from a good restaurant instead of McDonald's.
How homeowners defeated Zillow’s AI, which led to Zillow Offers’ demise https://t.co/YebMrmhdDN
My recent talk at the NSF town hall focused on the history of the AI winters, how the ML community became "anti-science," and whether the rejection of science will cause a winter for ML theory. I'll summarize these issues below...🧵
This article explains how to engineer relationships following 3 principles
What Every Engineer and Computer Scientist Should Know: The Biggest Contributor to Happiness
https://t.co/kRP8hklVnr
Evaluating ML models by computing a metric over a test set is not enough.
Critical to identify sub populations within a test set (ie segments/slices) and examine model performance on those subsets.
But how do you identify the subsets? Here’s 3 ways:
The last year saw the consolidation of some important concepts in Machine Learning. I took some time over Christmas to summarise the macro trends I'm most excited about heading into 2022. I'll share of the themes covered in the thread below 🧵
https://t.co/qrhRcNwiiR
itag's AI Summit 2021 25th Nov @ 11:45
Guest Speaker: Edmund Sutcliffe
Deterministic Construction Services: Why this is key to regulatory compliance and safety.
Book Now https://t.co/P9xLcElBpL
#innovation#ai#tech#OurTechCommunity#itagTech#TechTalks
🗨️TALK HIGHLIGHT! "Enhancing Customer Experience with AI/ML" by @emir_munoz
Join us for this Hybrid (in-person & virtual) event
📅 Saturday, January 29, 2022
🎟️ https://t.co/LygINQQoHR
🌍 London
#devfestuki#ai#ml#customerexperience
📢SPEAKER ANNOUNCEMENT! @emir_munoz is delighted to be one of our speakers for #DevFest UK & Ireland!
📅 Saturday, January 29, 2022
🎟️ https://t.co/LygINQQoHR
🌍 London
Join us for this Hybrid (in-person & virtual) event.
#devfestuki#ai#ml#CustomerExperience
With a 'can do' attitude we can achieve many things.
@Crowd4access was once only an idea in my mind, got early support of my colleague @VenkateshGM20 and @insight_centre. Today people are out in the streets to map footpath access for all.
I cannot state how proud I feel ☺️