Forwarded from Machine Learning with Python
A new collection of free courses has been added:
π 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. π
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. π§
What's inside:
β’ Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
β’ A table with lectures, descriptions, videos, notes, and authors
β’ Links to the original lectures and accompanying notes
β’ WIP markers for incomplete materials
β’ Instructions for contributors on adding and improving notes
The idea was appreciated. π
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. πΊοΈ
#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource
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βοΈ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
π Level up your AI & Data Science skills with HelloEncyclo β a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
β 13 courses live + 40+ coming soon
π― One access, lifetime updates
π Use code: PRESALE-BOOK-WAVE-2GFG
π https://helloencyclo.com/?ref=HUSSEINSHEIKHO
π 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. π
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. π§
What's inside:
β’ Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
β’ A table with lectures, descriptions, videos, notes, and authors
β’ Links to the original lectures and accompanying notes
β’ WIP markers for incomplete materials
β’ Instructions for contributors on adding and improving notes
The idea was appreciated. π
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. πΊοΈ
#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource
β¨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
βοΈ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
π Level up your AI & Data Science skills with HelloEncyclo β a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
β 13 courses live + 40+ coming soon
π― One access, lifetime updates
π Use code: PRESALE-BOOK-WAVE-2GFG
π https://helloencyclo.com/?ref=HUSSEINSHEIKHO
GitHub
GitHub - dair-ai/ML-Course-Notes: π Sharing machine learning course / lecture notes.
π Sharing machine learning course / lecture notes. - dair-ai/ML-Course-Notes
β€3
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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.
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:
β Where do I start?
π€ Do I need deep math first?
π§ Should I focus on ML, LLMs, RAG, or AI agents?
π§ How do I avoid wasting time learning the wrong things?
π How do I go from learning to becoming hireable?
If youβre interested in AI Engineering but unsure how to approach it, this livestream is for you.
What youβll learn
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β¦ Where beginners should start
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β¦ How to think about becoming hireable in AI
β¦ Practical advice from someone already working in the field
Sign up for FREE and save your seat: luma.com/qgz4g4r7
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:
β Where do I start?
π€ Do I need deep math first?
π§ Should I focus on ML, LLMs, RAG, or AI agents?
π§ How do I avoid wasting time learning the wrong things?
π How do I go from learning to becoming hireable?
If youβre interested in AI Engineering but unsure how to approach it, this livestream is for you.
What youβll learn
β¦ What AI Engineering really is
β¦ Where beginners should start
β¦ What skills and topics actually matter
β¦ Common mistakes to avoid
β¦ Self-study vs bootcamp vs MSc
β¦ How to think about becoming hireable in AI
β¦ Practical advice from someone already working in the field
Sign up for FREE and save your seat: luma.com/qgz4g4r7
β€1
Parallax: A Parameterized Local Linear Attention That Keeps Softmax and Adds a Learned Covariance Correction Branch π§ β¨
The Transformerβs 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. π
A team of researchers from Northwestern University, Tilde Research, and University of Washington introduce a parameterized Local Linear Attention called βParallaxβ that scales to LLM pretraining and codesigns with Muon. π
Parallax does not chase efficiency by cutting compute. It adds compute deliberately, then makes that compute cheaper to run on modern GPUs. π»β‘
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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βοΈ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
π Level up your AI & Data Science skills with HelloEncyclo β a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
β 13 courses live + 40+ coming soon
π― One access, lifetime updates
π Use code: PRESALE-BOOK-WAVE-2GFG
π https://helloencyclo.com/?ref=HUSSEINSHEIKHO
The Transformerβs 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. π
A team of researchers from Northwestern University, Tilde Research, and University of Washington introduce a parameterized Local Linear Attention called βParallaxβ that scales to LLM pretraining and codesigns with Muon. π
Parallax does not chase efficiency by cutting compute. It adds compute deliberately, then makes that compute cheaper to run on modern GPUs. π»β‘
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
β¨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
βοΈ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
π Level up your AI & Data Science skills with HelloEncyclo β a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
β 13 courses live + 40+ coming soon
π― One access, lifetime updates
π Use code: PRESALE-BOOK-WAVE-2GFG
π https://helloencyclo.com/?ref=HUSSEINSHEIKHO
β€5
If you already have 200 open tabs with courses, articles, and GitHub repositories on ML, this repository might save the situation a bit. π
Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. π€
Instead of endless Google searches, everything is organized into categories:
β’ fundamentals of machine learning
β’ neural networks and modern architectures
β’ tasks and application areas
β’ datasets
β’ libraries and tools
β’ fairness and AI ethics
β’ production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. π
I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. β οΈ
https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources
β¨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
βοΈ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
π Level up your AI & Data Science skills with HelloEncyclo β a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
β 13 courses live + 40+ coming soon
π― One access, lifetime updates
π Use code: PRESALE-BOOK-WAVE-2GFG
π https://helloencyclo.com/?ref=HUSSEINSHEIKHO
Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. π€
Instead of endless Google searches, everything is organized into categories:
β’ fundamentals of machine learning
β’ neural networks and modern architectures
β’ tasks and application areas
β’ datasets
β’ libraries and tools
β’ fairness and AI ethics
β’ production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. π
I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. β οΈ
https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources
β¨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
βοΈ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
π Level up your AI & Data Science skills with HelloEncyclo β a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
β 13 courses live + 40+ coming soon
π― One access, lifetime updates
π Use code: PRESALE-BOOK-WAVE-2GFG
π https://helloencyclo.com/?ref=HUSSEINSHEIKHO
β€2
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.
β Expert-curated content
β Lifetime access options
β Learn at your own pace
β Career-focused learning paths
β Regular content updates
β Affordable pricing
π Exclusive Offer: Get FLAT 45% OFF on all courses using my referral link:
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π’ Stay updated with new course launches, discounts, learning resources, interview preparation tips, and career guidance:
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π² WhatsApp Community:
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Whether you're preparing for your next job, aiming for a promotion, earning certifications, or simply upgrading your skills, HelloEncyclo is here to support
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Someone spent several months manually writing a 200-page guide on mathematics and the basics of machine learning. π
No marketing fluff or endless links between articles. Just an attempt to gather all the most important things in one place. π―
Inside:
β’ neural networks: backpropagation, SGD, Adam, BatchNorm; βοΈ
β’ classic ML: SVM, Gradient Boosting, K-Means, PCA; π
β’ hardware for AI: Tensor Cores, Systolic Arrays, CUDA; π₯οΈ
β’ transformers: Multi-Head Attention, KV Cache, LoRA; π§
β’ computer vision: ViT, CNN, MAE, IoU, NMS, VLM; ποΈ
β’ agent systems: ReAct, memory, orchestration, OpenClaw. π€
The author describes it as the material he would have wanted to receive himself several years ago. π°οΈ
And yes, the entire guide is distributed free of charge. π
https://www.arjunvirk.com/writing/ml-guide
#MachineLearning #AI #DeepLearning #DataScience #NeuralNetworks #Tech
β¨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
βοΈ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
π Level up your AI & Data Science skills with HelloEncyclo β a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
β 13 courses live + 40+ coming soon
π― One access, lifetime updates
π Use code: PRESALE-BOOK-WAVE-2GFG
π 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. π―
Inside:
β’ neural networks: backpropagation, SGD, Adam, BatchNorm; βοΈ
β’ classic ML: SVM, Gradient Boosting, K-Means, PCA; π
β’ hardware for AI: Tensor Cores, Systolic Arrays, CUDA; π₯οΈ
β’ transformers: Multi-Head Attention, KV Cache, LoRA; π§
β’ computer vision: ViT, CNN, MAE, IoU, NMS, VLM; ποΈ
β’ agent systems: ReAct, memory, orchestration, OpenClaw. π€
The author describes it as the material he would have wanted to receive himself several years ago. π°οΈ
And yes, the entire guide is distributed free of charge. π
https://www.arjunvirk.com/writing/ml-guide
#MachineLearning #AI #DeepLearning #DataScience #NeuralNetworks #Tech
β¨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
βοΈ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
π Level up your AI & Data Science skills with HelloEncyclo β a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
β 13 courses live + 40+ coming soon
π― One access, lifetime updates
π Use code: PRESALE-BOOK-WAVE-2GFG
π https://helloencyclo.com/?ref=HUSSEINSHEIKHO
β€3
Forwarded from Machine Learning with Python
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π A large collection of AI projects for practice
We found a repository that will help you move from theory to real development of AI applications.
Inside are dozens of ready-made projects: AI analytics, RAG systems, OCR applications, code review agents, travel assistants, and much more.
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π Level up your AI & Data Science skills with HelloEncyclo β a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
β 13 courses live + 40+ coming soon
π― One access, lifetime updates
π Use code: PRESALE-BOOK-WAVE-2GFG
π https://helloencyclo.com/?ref=HUSSEINSHEIKHO
We found a repository that will help you move from theory to real development of AI applications.
Inside are dozens of ready-made projects: AI analytics, RAG systems, OCR applications, code review agents, travel assistants, and much more.
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β¨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
βοΈ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
π Level up your AI & Data Science skills with HelloEncyclo β a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
β 13 courses live + 40+ coming soon
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π Use code: PRESALE-BOOK-WAVE-2GFG
π https://helloencyclo.com/?ref=HUSSEINSHEIKHO
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Multi-Label Text Classification with Scikit-LLM π
In this article, you will learn how to perform multi-label text classification using large language models and the scikit-LLM library, without the need for labeled training data or complex model training. π
Topics we will cover include:
What multi-label classification is and why it matters for nuanced text analysis. π
How to set up and configure scikit-LLM with a free, open-source LLM from Groq for zero-shot inference. βοΈ
How to load a real-world dataset and run multi-label sentiment predictions using a familiar scikit-learn-style workflow. π
Read: https://machinelearningmastery.com/multi-label-text-classification-with-scikit-llm/ π
#ScikitLLM #TextClassification #LLM #MachineLearning #ZeroShot #DataScience
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βοΈ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
π Level up your AI & Data Science skills with HelloEncyclo β a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
β 13 courses live + 40+ coming soon
π― One access, lifetime updates
π Use code: PRESALE-BOOK-WAVE-2GFG
π https://helloencyclo.com/?ref=HUSSEINSHEIKHO
In this article, you will learn how to perform multi-label text classification using large language models and the scikit-LLM library, without the need for labeled training data or complex model training. π
Topics we will cover include:
What multi-label classification is and why it matters for nuanced text analysis. π
How to set up and configure scikit-LLM with a free, open-source LLM from Groq for zero-shot inference. βοΈ
How to load a real-world dataset and run multi-label sentiment predictions using a familiar scikit-learn-style workflow. π
Read: https://machinelearningmastery.com/multi-label-text-classification-with-scikit-llm/ π
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β¨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
βοΈ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
π Level up your AI & Data Science skills with HelloEncyclo β a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
β 13 courses live + 40+ coming soon
π― One access, lifetime updates
π Use code: PRESALE-BOOK-WAVE-2GFG
π https://helloencyclo.com/?ref=HUSSEINSHEIKHO
β€2