🗂 A fresh deep learning course from MIT is now publicly available
A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
➡️ Link to the course
tags: #Python #DataScience #DeepLearning #AI
A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
➡️ Link to the course
tags: #Python #DataScience #DeepLearning #AI
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How a CNN sees images simplified 🧠
1. Input → Image breaks into pixels (RGB numbers)
2. Feature Extraction
· Convolution → Detects edges/patterns
· ReLU → Kills negatives, adds non-linearity
· Pooling → Shrinks data, keeps what matters
3. Fully Connected → Flattens features into meaning
4. Output → Probability scores: Cat? Dog? Car?
Why powerful: Learns hierarchically — edges → shapes → objects
Pixels to predictions. That's it. 👇
#DeepLearning #CNN #ComputerVision #AI
https://xn--r1a.website/CodeProgrammer
1. Input → Image breaks into pixels (RGB numbers)
2. Feature Extraction
· Convolution → Detects edges/patterns
· ReLU → Kills negatives, adds non-linearity
· Pooling → Shrinks data, keeps what matters
3. Fully Connected → Flattens features into meaning
4. Output → Probability scores: Cat? Dog? Car?
Why powerful: Learns hierarchically — edges → shapes → objects
Pixels to predictions. That's it. 👇
#DeepLearning #CNN #ComputerVision #AI
https://xn--r1a.website/CodeProgrammer
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Stop asking "CNN or VLM?" — the answer is both. 🤔
Everyone's talking about Vision Language Models replacing traditional computer vision. 📢
Here's the reality: they're not replacing anything. They're expanding what's possible. 🚀
CNNs are excellent at precise perception — detecting, localizing, classifying fixed objects at high speed and low cost. 🎯
Vision Language Models are better at interpretation — answering open-ended questions about a scene that you can't define as fixed labels in advance. 🧠
The smartest production systems combine both:
→ A lightweight CNN runs first (fast, cheap) ⚡️
→ A VLM handles the complex reasoning (flexible, expensive) 💎
This is the difference between giving machines eyes 👁 vs giving them the ability to talk about what they see. 🗣
Dr. Satya Mallick breaks it down in under 2 minutes. 👇
#ComputerVision #AI #MachineLearning #VisionLanguageModel #DeepLearning #OpenCV #AIEngineering
https://xn--r1a.website/CodeProgrammer✅
Everyone's talking about Vision Language Models replacing traditional computer vision. 📢
Here's the reality: they're not replacing anything. They're expanding what's possible. 🚀
CNNs are excellent at precise perception — detecting, localizing, classifying fixed objects at high speed and low cost. 🎯
Vision Language Models are better at interpretation — answering open-ended questions about a scene that you can't define as fixed labels in advance. 🧠
The smartest production systems combine both:
→ A lightweight CNN runs first (fast, cheap) ⚡️
→ A VLM handles the complex reasoning (flexible, expensive) 💎
This is the difference between giving machines eyes 👁 vs giving them the ability to talk about what they see. 🗣
Dr. Satya Mallick breaks it down in under 2 minutes. 👇
#ComputerVision #AI #MachineLearning #VisionLanguageModel #DeepLearning #OpenCV #AIEngineering
https://xn--r1a.website/CodeProgrammer
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🚀 Demystifying Activation Functions! 🧠✨
Ever wondered why activation functions are so critical in neural networks? 🤔🤖
They’re the secret sauce that allows models to capture complex, nonlinear relationships! 🔥📈
Do you want to learn how to implement an artificial neural network from scratch in Python using NumPy? 🐍📊
Learn more in super-detailed guide: https://lnkd.in/e4CydTtB 🔗📚
#NeuralNetworks #DeepLearning #ActivationFunctions #Python #NumPy #AI
Ever wondered why activation functions are so critical in neural networks? 🤔🤖
They’re the secret sauce that allows models to capture complex, nonlinear relationships! 🔥📈
Do you want to learn how to implement an artificial neural network from scratch in Python using NumPy? 🐍📊
Learn more in super-detailed guide: https://lnkd.in/e4CydTtB 🔗📚
#NeuralNetworks #DeepLearning #ActivationFunctions #Python #NumPy #AI
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"Dive into Deep Learning" 📘🤖 is an open-source book that forms the mathematical foundation for large language models. 🧠📐
It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. 🧮📉🔄
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. 🚀🔗🧠
It contains over 1,000 pages 📖 and provides clear explanations, practical examples, and exercises. ✅📝 Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. 🌐🔍🤖
arxiv.org/pdf/2106.11342 🔗
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
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It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. 🧮📉🔄
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. 🚀🔗🧠
It contains over 1,000 pages 📖 and provides clear explanations, practical examples, and exercises. ✅📝 Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. 🌐🔍🤖
arxiv.org/pdf/2106.11342 🔗
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
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Interactive Explainer 🧠✨
The Anatomy of an LLM 🔍
A visual walk through the machinery inside a large language model: from raw text, to tokens, to vectors, to attention, to the next token. ⚙️🧬
🔗 Link: https://www.royvanrijn.com/anatomy-of-an-llm/
#LLM #AI #Tech #NeuralNetworks #MachineLearning #DeepLearning
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The Anatomy of an LLM 🔍
A visual walk through the machinery inside a large language model: from raw text, to tokens, to vectors, to attention, to the next token. ⚙️🧬
🔗 Link: https://www.royvanrijn.com/anatomy-of-an-llm/
#LLM #AI #Tech #NeuralNetworks #MachineLearning #DeepLearning
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
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Roy van Rijn
The Anatomy of an LLM | Interactive Visual Guide to How Language Models Work
An interactive visual explainer for developers showing how LLMs work, from tokenization and embeddings to attention, transformers, training, KV cache, and quantization.
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