Best YouTube channels to learn AI https://t.co/DlEhiltUJ4
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Best ML combo 👇
Andrew Ng + StatQuest
Structure + intuition = ML that actually makes sense https://t.co/UoPAbFOisr
Andrew Ng + StatQuest
Structure + intuition = ML that actually makes sense https://t.co/UoPAbFOisr
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Sharing my go-to math resources for machine learning. https://t.co/15LYHVQ5sw
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Day 17 Of learning ML PreQuests:
1. Linear Algebra(Pass 2)
-completed eigen stuff
-on PCA..Pretty chill
2. Python(wasted)
-did just 2 Problems..
3. Math for ML book
- Red till scalar multiplication of vectors
(till now reading is pretty chill because its like Linear Algebra
1. Linear Algebra(Pass 2)
-completed eigen stuff
-on PCA..Pretty chill
2. Python(wasted)
-did just 2 Problems..
3. Math for ML book
- Red till scalar multiplication of vectors
(till now reading is pretty chill because its like Linear Algebra
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I kinda Wasted 30th and 31st of Jan,
Day 14 of Learning ML PreQuests
(Sunday Backlog completing)
1. Linear Algebra -
-Still on lecture 3.. 40 mins left
-determinant is cool
2. Python
-List lec done
-task 4 completed (proud of it)
-list comprehension is cool
Day 14 of Learning ML PreQuests
(Sunday Backlog completing)
1. Linear Algebra -
-Still on lecture 3.. 40 mins left
-determinant is cool
2. Python
-List lec done
-task 4 completed (proud of it)
-list comprehension is cool
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MiniMax Agent = Claude Cowork + Agent skills + Moltbot (Clawdbot)
@MiniMax_AI just shipped MiniMax Agent Desktop, and it’s one of the first "AI workspaces" that actually feels like a real digital coworker.
It's very helpful for learning the latest AI concepts!
Watch how I used https://t.co/QaxGa3Aicg
@MiniMax_AI just shipped MiniMax Agent Desktop, and it’s one of the first "AI workspaces" that actually feels like a real digital coworker.
It's very helpful for learning the latest AI concepts!
Watch how I used https://t.co/QaxGa3Aicg
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I am still in this thanks to God✨
Day 11 of Learning ML PreQuests🪼
1. Linear Algebra(Pass 2)🐍
-on matrix part, 12th class stuff
2. Python🐟
-Completed lec 3
-On time complexity
I have a physics exam on 29th, rotational and oscillation are coming.🤖
Day 11 of Learning ML PreQuests🪼
1. Linear Algebra(Pass 2)🐍
-on matrix part, 12th class stuff
2. Python🐟
-Completed lec 3
-On time complexity
I have a physics exam on 29th, rotational and oscillation are coming.🤖
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Day 9️⃣ of Learning ML PreQuests
1. Linear Algebra(Pass 2)
-Completed Lecture 1 notes.(Except Hyperplane equation.)
2. Python
-Completed 1 more lecture(it was like revision)
Well Sundayy noww, lets goo.
1. Linear Algebra(Pass 2)
-Completed Lecture 1 notes.(Except Hyperplane equation.)
2. Python
-Completed 1 more lecture(it was like revision)
Well Sundayy noww, lets goo.
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Day 🎱 of Learning ML PreQuests🪼
1- Linear Algebra (Pass 1 completed today)🐍
-this is completed, Now i will do pass 2 with notes
2- Python 🐟
-Did 1.5 lectures + question practice
I hope to start NumPy on 1 Feb and Statistics (Pass 1)
Doing DSMP a lot.
1- Linear Algebra (Pass 1 completed today)🐍
-this is completed, Now i will do pass 2 with notes
2- Python 🐟
-Did 1.5 lectures + question practice
I hope to start NumPy on 1 Feb and Statistics (Pass 1)
Doing DSMP a lot.
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Day 7️⃣ of Learning ML PreQuests:🦆
#1. Linear Algebra(Pass 1)🙂
- some thing on Decomposition of smthing
- A 'derivation' of a formula you can say
#2 Python 🪼
-Completed Functions lecture
-They are first class Citizens🤖, haha
- map() and filter() are imp as I heard
#1. Linear Algebra(Pass 1)🙂
- some thing on Decomposition of smthing
- A 'derivation' of a formula you can say
#2 Python 🪼
-Completed Functions lecture
-They are first class Citizens🤖, haha
- map() and filter() are imp as I heard
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-Day 6️⃣ Of Learning PreQuests for ML🦆
1. Linear Algebra(Pass 1)📐
-I think I am doing Eigen values and PCA i guess.
-PCA is Deminishing Higher D to Low D
2. Python (Functions)🐍
-Dude I realised how much I didn't know in Python
-yk there is a thing called sets in python? https://t.co/azmBwHp7Qh
1. Linear Algebra(Pass 1)📐
-I think I am doing Eigen values and PCA i guess.
-PCA is Deminishing Higher D to Low D
2. Python (Functions)🐍
-Dude I realised how much I didn't know in Python
-yk there is a thing called sets in python? https://t.co/azmBwHp7Qh
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-Day 5️⃣ Of Learning PreQuest For ML:🦆
1. Linear Algebra (Pass 1):🙂
-Learned Eigen Vectors and stuff(pretty chill)
-kinda hard easy i think 🗿
2. Python (Yeah DSMP)🐌
-But Doing from functions. (thought to refine my skills and knowledge a bit)
-*args and **kwargs are nice🔥 https://t.co/aUuDar7ceW
1. Linear Algebra (Pass 1):🙂
-Learned Eigen Vectors and stuff(pretty chill)
-kinda hard easy i think 🗿
2. Python (Yeah DSMP)🐌
-But Doing from functions. (thought to refine my skills and knowledge a bit)
-*args and **kwargs are nice🔥 https://t.co/aUuDar7ceW
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As regulations (EU AI Act, U.S. sectoral frameworks, privacy laws) tighten, AI systems must generate explainability, risk reports, model cards, and compliance evidence automatically.
➡️ Technical focus:
• Automated tracking of datasets, lineage, and deployment contexts
•
➡️ Technical focus:
• Automated tracking of datasets, lineage, and deployment contexts
•
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Beyond statistics, AI is learning to represent cause–effect structure in data. This moves models from correlation to genuine reasoning.
➡️ Key idea: Instead of just mapping inputs to outputs, models infer latent causal factors and how interventions influence outcomes.
➡️ Why it
➡️ Key idea: Instead of just mapping inputs to outputs, models infer latent causal factors and how interventions influence outcomes.
➡️ Why it
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AI in robotics is moving from offline planning to real-time, adaptive control using learned models. These systems use predictive models to make decisions under uncertainty.
➡️ Core technique: Model-based reinforcement learning plus differentiable physics simulators.
➡️ What’s
➡️ Core technique: Model-based reinforcement learning plus differentiable physics simulators.
➡️ What’s
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Instead of separate vision, language, or audio models, unified architectures learn cross-modal reasoning that blends modalities simultaneously.
➡️ What’s new in 2026: Models now process text, images, video, audio, and sensor data in one shared representation.
➡️ Why it matters:
➡️ What’s new in 2026: Models now process text, images, video, audio, and sensor data in one shared representation.
➡️ Why it matters:
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Traditional AI learns once and then stays static. Continual learning aims to update a model incrementally without forgetting prior knowledge.
➡️ Core idea: Models maintain memory of past tasks while integrating new ones. This is crucial for systems that must adapt to evolving
➡️ Core idea: Models maintain memory of past tasks while integrating new ones. This is crucial for systems that must adapt to evolving
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As foundation models grow enormous, training and running them becomes costly. Distillation creates smaller specialist models that retain performance on target tasks.
➡️ How it works: A large teacher model generates target outputs (e.g., embeddings or labels) on curated data. A
➡️ How it works: A large teacher model generates target outputs (e.g., embeddings or labels) on curated data. A
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16 ~ Learning ML(PreQuests) | exJEE Aspirant :) Trying.. Stay Humble
6 Followers
8 Contributions
ML Engineer | Building end-to-end pipelines, automation systems, and GenAI-powered workflows.....
438 Followers
3 Contributions
AI/ML Engineer AI Notes: https://t.co/lC2UKMtRjj Youtube: https://t.co/pjpX8NvUn5 Newsletter: https://t.co/NMMvPSmzua
89.9K Followers
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