If you want to finally understand how neural networks actually learn, I recommend these notes from Stanford CS224N. ๐ง
"Computing Neural Network Gradients" explains the calculation of gradients and backpropagation without black-box formulas. ๐
Inside:
โข Chain Rule
โข Computational Graphs
โข Vectorized derivatives
โข Efficient gradient calculation
โข Step-by-step examples with formula analysis
Many people use PyTorch or TensorFlow every day, but never understood what happens after calling .backward(). ๐ฅ
These notes just fill this gap. ๐ ๏ธ
PDF:
https://web.stanford.edu/class/cs224n/readings/gradient-notes.pdf
#NeuralNetworks #DeepLearning #StanfordCS #Backpropagation #MachineLearning #AIResearch
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โ 13 courses live + 40+ coming soon
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๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
"Computing Neural Network Gradients" explains the calculation of gradients and backpropagation without black-box formulas. ๐
Inside:
โข Chain Rule
โข Computational Graphs
โข Vectorized derivatives
โข Efficient gradient calculation
โข Step-by-step examples with formula analysis
Many people use PyTorch or TensorFlow every day, but never understood what happens after calling .backward(). ๐ฅ
These notes just fill this gap. ๐ ๏ธ
PDF:
https://web.stanford.edu/class/cs224n/readings/gradient-notes.pdf
#NeuralNetworks #DeepLearning #StanfordCS #Backpropagation #MachineLearning #AIResearch
โจ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
โญ๏ธ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
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๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
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Forwarded from Machine Learning with Python
Data Science Interview Questions.pdf
1.4 MB
Data Science Interview Questions
๐ก Here is your curated list for Data Science interviews!
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๐ก Here is your curated list for Data Science interviews!
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โญ๏ธ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Forwarded from Machine Learning with Python
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๐ Lifetime access to HelloEncyclo โ every AI, ML & Data Science course ever built โ for ~$41. Once. Forever.
This isn't a drill. This isn't a rerun.
This is the founding-member price โ and it disappears the moment the first 250 seats globally are gone.
โ 13 courses live right now
โ 40+ more in 2โ3 weeks
โ Every future course included automatically
โ 15-day money-back โ full refund, no questions
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Forwarded from Machine Learning with Python
A new collection of free courses has been added:
๐ https://github.com/dair-ai/ML-Course-Notes
Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. ๐
Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. ๐ง
What's inside:
โข Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
โข A table with lectures, descriptions, videos, notes, and authors
โข Links to the original lectures and accompanying notes
โข WIP markers for incomplete materials
โข Instructions for contributors on adding and improving notes
The idea was appreciated. ๐
Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. ๐บ๏ธ
#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource
โจ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
โญ๏ธ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
๐ https://github.com/dair-ai/ML-Course-Notes
Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. ๐
Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. ๐ง
What's inside:
โข Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
โข A table with lectures, descriptions, videos, notes, and authors
โข Links to the original lectures and accompanying notes
โข WIP markers for incomplete materials
โข Instructions for contributors on adding and improving notes
The idea was appreciated. ๐
Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. ๐บ๏ธ
#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource
โจ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
โญ๏ธ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
โ 13 courses live + 40+ coming soon
๐ฏ One access, lifetime updates
๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
GitHub
GitHub - dair-ai/ML-Course-Notes: ๐ Sharing machine learning course / lecture notes.
๐ Sharing machine learning course / lecture notes. - dair-ai/ML-Course-Notes
โค3
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Join our livestream with Marina Wyss, Senior Applied Scientist at Twitch, as we discuss how to break into AI Engineering in 2026.
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Many people interested in AI Engineering are asking the same questions:
โ Where do I start?
๐ค Do I need deep math first?
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โฆ Practical advice from someone already working in the field
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Sign up for FREE and save your seat here: luma.com/qgz4g4r7
Why should you join?
Many people interested in AI Engineering are asking the same questions:
โ Where do I start?
๐ค Do I need deep math first?
๐ง Should I focus on ML, LLMs, RAG, or AI agents?
๐งญ How do I avoid wasting time learning the wrong things?
๐ How do I go from learning to becoming hireable?
If youโre interested in AI Engineering but unsure how to approach it, this livestream is for you.
What youโll learn
โฆ What AI Engineering really is
โฆ Where beginners should start
โฆ What skills and topics actually matter
โฆ Common mistakes to avoid
โฆ Self-study vs bootcamp vs MSc
โฆ How to think about becoming hireable in AI
โฆ Practical advice from someone already working in the field
Sign up for FREE and save your seat: luma.com/qgz4g4r7
โค1
Parallax: A Parameterized Local Linear Attention That Keeps Softmax and Adds a Learned Covariance Correction Branch ๐ง โจ
The Transformerโs 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. ๐
A team of researchers from Northwestern University, Tilde Research, and University of Washington introduce a parameterized Local Linear Attention called โParallaxโ that scales to LLM pretraining and codesigns with Muon. ๐
Parallax does not chase efficiency by cutting compute. It adds compute deliberately, then makes that compute cheaper to run on modern GPUs. ๐ปโก
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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๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
The Transformerโs 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. ๐
A team of researchers from Northwestern University, Tilde Research, and University of Washington introduce a parameterized Local Linear Attention called โParallaxโ that scales to LLM pretraining and codesigns with Muon. ๐
Parallax does not chase efficiency by cutting compute. It adds compute deliberately, then makes that compute cheaper to run on modern GPUs. ๐ปโก
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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โค5
If you already have 200 open tabs with courses, articles, and GitHub repositories on ML, this repository might save the situation a bit. ๐
Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. ๐ค
Instead of endless Google searches, everything is organized into categories:
โข fundamentals of machine learning
โข neural networks and modern architectures
โข tasks and application areas
โข datasets
โข libraries and tools
โข fairness and AI ethics
โข production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. ๐
I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. โ ๏ธ
https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources
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โ 13 courses live + 40+ coming soon
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๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. ๐ค
Instead of endless Google searches, everything is organized into categories:
โข fundamentals of machine learning
โข neural networks and modern architectures
โข tasks and application areas
โข datasets
โข libraries and tools
โข fairness and AI ethics
โข production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. ๐
I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. โ ๏ธ
https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources
โจ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
โญ๏ธ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
โค2
Forwarded from Vinayak Chiluka
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At HelloEncyclo, we're building a comprehensive AI-powered learning platform designed to help students, professionals, and career switchers gain practical, industry-relevant skills through structured learning paths.
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