If you want to finally understand how neural networks actually learn, I recommend these notes from Stanford CS224N. 🧠
"Computing Neural Network Gradients" explains the calculation of gradients and backpropagation without black-box formulas. 📉
Inside:
• Chain Rule
• Computational Graphs
• Vectorized derivatives
• Efficient gradient calculation
• Step-by-step examples with formula analysis
Many people use PyTorch or TensorFlow every day, but never understood what happens after calling .backward(). 🔥
These notes just fill this gap. 🛠️
PDF:
https://web.stanford.edu/class/cs224n/readings/gradient-notes.pdf
#NeuralNetworks #DeepLearning #StanfordCS #Backpropagation #MachineLearning #AIResearch
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"Computing Neural Network Gradients" explains the calculation of gradients and backpropagation without black-box formulas. 📉
Inside:
• Chain Rule
• Computational Graphs
• Vectorized derivatives
• Efficient gradient calculation
• Step-by-step examples with formula analysis
Many people use PyTorch or TensorFlow every day, but never understood what happens after calling .backward(). 🔥
These notes just fill this gap. 🛠️
PDF:
https://web.stanford.edu/class/cs224n/readings/gradient-notes.pdf
#NeuralNetworks #DeepLearning #StanfordCS #Backpropagation #MachineLearning #AIResearch
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
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❤2
Parallax: A Parameterized Local Linear Attention That Keeps Softmax and Adds a Learned Covariance Correction Branch 🧠✨
The Transformer’s attention mechanism has barely changed since 2017. Most efficiency work has tried to replace softmax attention outright. A new paper takes a different route. It keeps softmax attention and bolts on a correction branch. 🔄
A team of researchers from Northwestern University, Tilde Research, and University of Washington introduce a parameterized Local Linear Attention called ‘Parallax’ that scales to LLM pretraining and codesigns with Muon. 🎓
Parallax does not chase efficiency by cutting compute. It adds compute deliberately, then makes that compute cheaper to run on modern GPUs. 💻⚡
More: https://www.marktechpost.com/2026/05/31/parallax-a-parameterized-local-linear-attention-that-keeps-softmax-and-adds-a-learned-covariance-correction-branch/
#Parallax #LLM #AI #DeepLearning #Transformer #TechNews
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The Transformer’s attention mechanism has barely changed since 2017. Most efficiency work has tried to replace softmax attention outright. A new paper takes a different route. It keeps softmax attention and bolts on a correction branch. 🔄
A team of researchers from Northwestern University, Tilde Research, and University of Washington introduce a parameterized Local Linear Attention called ‘Parallax’ that scales to LLM pretraining and codesigns with Muon. 🎓
Parallax does not chase efficiency by cutting compute. It adds compute deliberately, then makes that compute cheaper to run on modern GPUs. 💻⚡
More: https://www.marktechpost.com/2026/05/31/parallax-a-parameterized-local-linear-attention-that-keeps-softmax-and-adds-a-learned-covariance-correction-branch/
#Parallax #LLM #AI #DeepLearning #Transformer #TechNews
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❤5
If you already have 200 open tabs with courses, articles, and GitHub repositories on ML, this repository might save the situation a bit. 😅
Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. 🤖
Instead of endless Google searches, everything is organized into categories:
• fundamentals of machine learning
• neural networks and modern architectures
• tasks and application areas
• datasets
• libraries and tools
• fairness and AI ethics
• production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. 📝
I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. ⚠️
https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources
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Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. 🤖
Instead of endless Google searches, everything is organized into categories:
• fundamentals of machine learning
• neural networks and modern architectures
• tasks and application areas
• datasets
• libraries and tools
• fairness and AI ethics
• production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. 📝
I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. ⚠️
https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
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🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
❤3
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Someone spent several months manually writing a 200-page guide on mathematics and the basics of machine learning. 📘
No marketing fluff or endless links between articles. Just an attempt to gather all the most important things in one place. 🎯
Inside:
• neural networks: backpropagation, SGD, Adam, BatchNorm; ⚙️
• classic ML: SVM, Gradient Boosting, K-Means, PCA; 📊
• hardware for AI: Tensor Cores, Systolic Arrays, CUDA; 🖥️
• transformers: Multi-Head Attention, KV Cache, LoRA; 🧠
• computer vision: ViT, CNN, MAE, IoU, NMS, VLM; 👁️
• agent systems: ReAct, memory, orchestration, OpenClaw. 🤖
The author describes it as the material he would have wanted to receive himself several years ago. 🕰️
And yes, the entire guide is distributed free of charge. 🆓
https://www.arjunvirk.com/writing/ml-guide
#MachineLearning #AI #DeepLearning #DataScience #NeuralNetworks #Tech
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
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🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
No marketing fluff or endless links between articles. Just an attempt to gather all the most important things in one place. 🎯
Inside:
• neural networks: backpropagation, SGD, Adam, BatchNorm; ⚙️
• classic ML: SVM, Gradient Boosting, K-Means, PCA; 📊
• hardware for AI: Tensor Cores, Systolic Arrays, CUDA; 🖥️
• transformers: Multi-Head Attention, KV Cache, LoRA; 🧠
• computer vision: ViT, CNN, MAE, IoU, NMS, VLM; 👁️
• agent systems: ReAct, memory, orchestration, OpenClaw. 🤖
The author describes it as the material he would have wanted to receive himself several years ago. 🕰️
And yes, the entire guide is distributed free of charge. 🆓
https://www.arjunvirk.com/writing/ml-guide
#MachineLearning #AI #DeepLearning #DataScience #NeuralNetworks #Tech
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
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🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
❤3
Forwarded from Machine Learning with Python
🎓 A Free AI Course for Beginners by Microsoft
For those just getting into artificial intelligence, Microsoft offers a free course.
It runs for 12 weeks and includes 24 lessons with theory, hands-on assignments, labs, and quizzes.
The curriculum covers neural networks and deep learning, computer vision, natural language processing, genetic algorithms, and AI ethics. For practice, it uses the two main ML frameworks—TensorFlow and PyTorch.
Each lesson follows the same structure: first, reading material, then a Jupyter notebook with code, and for some topics, a lab. The course is in English but has been translated into dozens of languages.
➡️ All materials and links are on GitHub
https://github.com/microsoft/AI-For-Beginners/blob/main/translations/ru/README.md
What's your AI level right now?
❤️ — Advanced user
🔥 — Almost zero
#AICourse #Microsoft #DeepLearning #TensorFlow #PyTorch #MachineLearning
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✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
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For those just getting into artificial intelligence, Microsoft offers a free course.
It runs for 12 weeks and includes 24 lessons with theory, hands-on assignments, labs, and quizzes.
The curriculum covers neural networks and deep learning, computer vision, natural language processing, genetic algorithms, and AI ethics. For practice, it uses the two main ML frameworks—TensorFlow and PyTorch.
Each lesson follows the same structure: first, reading material, then a Jupyter notebook with code, and for some topics, a lab. The course is in English but has been translated into dozens of languages.
➡️ All materials and links are on GitHub
https://github.com/microsoft/AI-For-Beginners/blob/main/translations/ru/README.md
What's your AI level right now?
❤️ — Advanced user
🔥 — Almost zero
#AICourse #Microsoft #DeepLearning #TensorFlow #PyTorch #MachineLearning
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
❤1