The scariest thing about GenAI isnβt the hallucination rate. Itβs the overconfidence in the outputs. A wrong answer said fluently is more dangerous than silence.
Trust layers and human-in-the-loop systems are now table stakes.
#AI #LLM #GenAI
30.10.2025 16:42 β π 1 π 0 π¬ 0 π 0
What's one tool you love in R but struggle to replicate in Python? For me: ggplot2 still sets the gold standard for layered, expressive graphics. Even after years of using matplotlib + seaborn.
#RStats #DataViz #DataScience #MachineLearning
29.10.2025 18:35 β π 10 π 1 π¬ 2 π 0
Everyoneβs watching LLM capabilities. But the real action? AI infrastructure. Foundry tools, vector DBs, fine-tuning stacks, and orchestration layers will define the next phase. The model is just the engine. The system wins the race.
#AI #LLM #GenAI
28.10.2025 19:24 β π 0 π 0 π¬ 0 π 0
When teaching regression, I always emphasize this:
The model is only as honest as your assumptions.
Check:
β Linearity
β Independence
β Homoscedasticity
β Normality
Violating these? Youβre interpreting fiction. #RStats
#DataScience #MachineLearning #AI
27.10.2025 21:58 β π 7 π 0 π¬ 1 π 0
GenAI isn't replacing data scientists. Itβs exposing which parts of our workflows were mechanical all along. Your future value? Critical thinking, domain insight, and narrative translation of data into decisions. Thatβs not automatable.
#AI #LLm #GenAI #DataScience
24.10.2025 18:19 β π 2 π 0 π¬ 1 π 0
Love your book. I recommend it to my data science classes.
24.10.2025 07:00 β π 0 π 0 π¬ 1 π 0
I still use R for many of my statistical workflows. Why?
β Transparent model behavior
β Robust packages like caret, ggplot2, dplyr
β Clean inferential workflows
#RStats is underrated for projects where explainability matters most.
#DataScience #MachineLearning #AI
23.10.2025 18:07 β π 6 π 0 π¬ 0 π 0
As I revised my book, I keep returning to this truth: Beginner data scientists donβt need more code. They need more understanding. Understanding distributions. Assumptions. Metrics. The craft is in the questions, not just the syntax.
#DataScience #MachineLearning #AI #RStats
22.10.2025 23:10 β π 14 π 0 π¬ 0 π 0
Ha! "Humans don't use reinforcement learning. Reinforcement learning is a lot worse than the average person thinks. Reinforcement learning is terrible. It just so happens that everything we had before is much worse." -- Andrej Karpathy
#AI #GenAI #LLM
21.10.2025 04:04 β π 0 π 0 π¬ 0 π 0
The promise of AI isnβt automation. Itβs augmentation.
A good system enhances human judgment. A great one earns it. The future belongs to teams that blend algorithms with reasoning.
#DataScience #MachineLearnign #AI #RStats
20.10.2025 17:47 β π 2 π 0 π¬ 0 π 0
Iβve seen brilliant models fail becauseβ¦
β the assumptions werenβt documented
β the business context changed
β no one owned it post-launch
ML is not a sprint. Itβs sustained operational discipline.
#DataScience #MachineLearning #AI #RStats
17.10.2025 23:39 β π 2 π 0 π¬ 0 π 0
If you had to remove one phase from the data science lifecycle to see what breaks firstβwhat would it be?
Data collection, cleaning, modeling, validation, or communication? (And no, you canβt say βnone.β)
#DataScience #MachineLearning #AI #RStats
17.10.2025 01:32 β π 3 π 1 π¬ 0 π 0
Cool quote: "Nobody needs atomic bombs, everyone needs AI." Jensen Huang, CEO @nvidia Here, here! #AI
16.10.2025 03:41 β π 0 π 0 π¬ 0 π 0
One of the most common student questions I hear at UCLA: βWhat algorithm should I use?β My answer:
βFirst ask: do you have the right data? The right framing? The right metric?β The algorithm comes last.
#DataScience #MachineLearning #AI #RStats
16.10.2025 00:16 β π 7 π 0 π¬ 1 π 0
Every project starts with a question. Most fail because the question was poorly defined, politically convenient, or technically impossible. The best data scientists challenge the question before answering it.
#DataScience #MachineLearning #AI #RStats
14.10.2025 03:29 β π 8 π 1 π¬ 0 π 0
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