Top Tweets for #MachineLarning
This AI ad is wild 😂
https://t.co/Oy2Sencny8
Sunday reading about Hilbert Space, Kernels, and Machine Learning : https://t.co/HrU9tjqxXw
#datascience #statistics #machinelarning
Feature selection
Pearson correlation
#machinelarning
Learning ML from scratch from campusX YouTube channel. Learned about the Offline batch learning and its limitations. Since the videos are too short so i cover 2,3 videos a day.
If anyone is already learning from him. Please do give suggestions :)
#machinelarning #campusX
*/ I constantly see people rise in life who are not the smartest, sometimes not even the most diligent……but they are learning machines. */
— Charlie Munger
> BTW 2025 isn’t for the genius.
> It’s for the obsessed learner.
#machinelarning #charliemunger
Take a read of this fascinating material. Some say the entire AI revolution is a bubble. I see it as a double-edged sword kinda
#AI #machinelarning
What if the AI investment boom goes wrong? The economic and financial pain will be swift and severe—but plenty will lose their shirts even if the technology achieves its potential https://t.co/kPiKirDcCY
#datavisualization #virtualassistant #LLM #language #machinelarning #learningmodels #learingmethod
4th International Conference on NLP and Machine Learning Trends (NLMLT 2025)
August 23 ~ 24, 2025, Dubai, UAE
https://t.co/prLyZbp13o
Submission URL : https://t.co/VhUmEMIYUC
R version of 'Backpropagating quasi-randomized neural networks':
- Backpropagation = finding artificial neural networks' weights
- Novelty = no analytical gradients + can use any machine learning model
https://t.co/C2NLItLGat
#rstats #machinelarning #Techtonique
#machinelarning #learningmodels #learingmethod
4th International Conference on NLP and Machine Learning Trends (NLMLT 2025)
August 23 ~ 24, 2025, Dubai, UAE
https://t.co/prLyZbp13o
Contact Us : [email protected]
Submission URL : https://t.co/VhUmEMIYUC
#machinelarning #learningmodels #learingmethod
4th International Conference on NLP and Machine Learning Trends (NLMLT 2025)
August 23 ~ 24, 2025, Dubai, UAE
https://t.co/prLyZbp13o
Submission URL : https://t.co/VhUmEMIYUC
When AI optimizes for the wrong goal, it can produce unexpected or harmful results-like a robot vacuum hiding dirt instead of cleaning it.
#codegene #techtrends #ai #machinelarning #technology #innovation #future #AIInnovation #NLP #logics #neuralnetworks #Robotics
Tomorrow is a special day, because it's Pi Day 🥧
To celebrate, go to 👉 https://t.co/zJk7fh3dXA tomorrow at 9AM PT for AWS Pi Day to learn more about data, analytics, and see demos from #AmazonSageMaker & #AmazonS3
See you there!
#AWS #ai #machinelarning
6th International Conference on Natural Language Computing and AI (NLCAI 2025)
May 24 ~ 25, 2025, Vancouver, Canada
#machinelarning #learningmodels #learingmethod
https://t.co/gdu8yullkR
Contact Us : [email protected]
Submission URL : https://t.co/KIdSjgbZ2k
6th International Conference on Natural Language Computing and AI (NLCAI 2025)
May 24 ~ 25, 2025, Vancouver, Canada
#machinelarning #learningmodels #learingmethod
https://t.co/gdu8yulTap
Contact Us : [email protected]
Paper Submission URL : https://t.co/KIdSjgcwRS
A Reminder of an old yet Gold Modeling Trick(Choosing the model) in #MachineLarning that never dies 🧑💻🤖🚀:
- Linear regression (l1/l2 as norms and elasticnet as regularization) as a starter.
- LightGBM for pretty much everything as it’s fast and accurate.
- CatBoost if you have many categorical features.
- Random Forest if your OOS variance is too high from LightGBM.
- XGBoost when you’re just curious.
- Ensemble multiple models and average the preds using postprocessing techniques like weighted average, median, etc, as well as run feature neutralisation.
Also, Random Forest is a Swiss knife of data science, albeit less powerful (but almost as powerful for most tasks unless one is going for that extra 0.001 on Kaggle) than boosted trees (CatBoost/LightGBM/XGBoost).
It is almost as powerful whilst much less fragile in terms of overfitting and less sensitive to hyperparameters. One can use Random Forest out of the box to get a good benchmark; less known is that XGBoost contains a faster implementation of Random Forest.
Note: Out-of-Specification (OOS) variance is when a sample's test results do not meet the established acceptance criteria.
Follow @trawasthi_ai for more contents that make ML, Deep Learning, Maths, etc. simple to understand.
#MachineLearning #DeepLearning #Maths #GenAI #100DaysOfCode
What would be the output of following Python Code🐍?
#python #quiz #100DaysOfCode #programming #pythonquiz #datascience #coder #machinelarning #artificialintelligence
This year's #NobelPrize announcement took the world by surprise, leaving many to wonder if #machinelarning and #AI can truly be considered "physics". We share one writer's perspective on both the history of AI and how it relates to physics: https://t.co/R6e9aegPM1
Automatically fine-tune parameter set-points in real-time & let our #AI Operator provide continuous process diagnosis, catching issues early and ensuring smooth, efficient operations. Stay ahead with intelligent, proactive control!
#PredictiveControl #MachineLarning #SaaS #Haber
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