These 9 courses covers LLMs, Agents, Deep RL, Audio and more
https://huggingface.co/learn/llm-course/chapter1/1
https://huggingface.co/learn/agents-course/unit0/introduction
https://huggingface.co/learn/deep-rl-course/unit0/introduction
https://huggingface.co/learn/cookbook/index
https://huggingface.co/learn/ml-games-course/unit0/introduction
https://huggingface.co/learn/audio-course/chapter0/introduction
https://huggingface.co/learn/computer-vision-course/unit0/welcome/welcome
https://huggingface.co/learn/ml-for-3d-course/unit0/introduction
https://huggingface.co/learn/diffusion-course/unit0/1
#HuggingFace #FreeCourses #AI #MachineLearning #DeepLearning #LLM #Agents #ReinforcementLearning #AudioAI #ComputerVision #3DAI #DiffusionModels #OpenSourceAIο»Ώ
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@codeprogrammer machine learning notes.pdf
21 MB
Best Machine Learning Notes
ο»Ώ
Join to our WhatsAppπ± channel:
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#HuggingFace #FreeCourses #AI #MachineLearning #DeepLearning #LLM #Agents #python #PythonProgramming #ReinforcementLearning #AudioAI #ComputerVision #3DAI #DiffusionModels #OpenSourceAI
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LLM Interview Questions.pdf
71.2 KB
Top 50 LLM Interview Questions!
#LLM #AIInterviews #MachineLearning #DeepLearning #NLP #LLMInterviewPrep #ModelArchitectures #AITheory #TechInterviews #MLBasics #InterviewQuestions #LargeLanguageModels
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π€π§ Build a Large Language Model From Scratch: A Step-by-Step Guide to Understanding and Creating LLMs
ποΈ 08 Oct 2025
π AI News & Trends
In recent years, Large Language Models (LLMs) have revolutionized the world of Artificial Intelligence (AI). From ChatGPT and Claude to Llama and Mistral, these models power the conversational systems, copilots, and generative tools that dominate todayβs AI landscape. However, for most developers and learners, the inner workings of these systems remain a mystery until now. ...
#LargeLanguageModels #LLM #ArtificialIntelligence #DeepLearning #MachineLearning #AIGuides
ποΈ 08 Oct 2025
π AI News & Trends
In recent years, Large Language Models (LLMs) have revolutionized the world of Artificial Intelligence (AI). From ChatGPT and Claude to Llama and Mistral, these models power the conversational systems, copilots, and generative tools that dominate todayβs AI landscape. However, for most developers and learners, the inner workings of these systems remain a mystery until now. ...
#LargeLanguageModels #LLM #ArtificialIntelligence #DeepLearning #MachineLearning #AIGuides
β€5
π Stanford has released a new course: βTransformers & Large Language Modelsβ
The authors are the Amidi brothers, and three free lectures are already available on YouTube. This is probably one of the most systematic introductory courses on modern LLMs.
Course content:
β’ Transformers: tokenization, embeddings, attention, architecture
β’ #LLM basics: Mixture of Experts, decoding types
β’ Training and fine-tuning: SFT, RL, LoRA
β’ Model evaluation: LLM/VLM-as-a-judge, best practices
β’ Tricks: RoPE, attention approximations, quantization
β’ Reasoning: scaling during training and inference
β’ Agentic approaches: #RAG, tool calling
If you are already familiar with this topic β itβs a great opportunity to refresh your knowledge and try implementing some techniques from scratch.
https://cme295.stanford.edu/syllabus/
https://xn--r1a.website/CodeProgrammerπ
The authors are the Amidi brothers, and three free lectures are already available on YouTube. This is probably one of the most systematic introductory courses on modern LLMs.
Course content:
β’ Transformers: tokenization, embeddings, attention, architecture
β’ #LLM basics: Mixture of Experts, decoding types
β’ Training and fine-tuning: SFT, RL, LoRA
β’ Model evaluation: LLM/VLM-as-a-judge, best practices
β’ Tricks: RoPE, attention approximations, quantization
β’ Reasoning: scaling during training and inference
β’ Agentic approaches: #RAG, tool calling
If you are already familiar with this topic β itβs a great opportunity to refresh your knowledge and try implementing some techniques from scratch.
https://cme295.stanford.edu/syllabus/
https://xn--r1a.website/CodeProgrammer
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π€π§ Mastering Large Language Models: Top #1 Complete Guide to Maxime Labonneβs LLM Course
ποΈ 22 Oct 2025
π AI News & Trends
In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have become the foundation of modern AI innovation powering tools like ChatGPT, Claude, Gemini and countless enterprise AI applications. However, building, fine-tuning and deploying these models require deep technical understanding and hands-on expertise. To bridge this knowledge gap, Maxime Labonne, a leading AI ...
#LLM #ArtificialIntelligence #MachineLearning #DeepLearning #AIEngineering #LargeLanguageModels
ποΈ 22 Oct 2025
π AI News & Trends
In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) have become the foundation of modern AI innovation powering tools like ChatGPT, Claude, Gemini and countless enterprise AI applications. However, building, fine-tuning and deploying these models require deep technical understanding and hands-on expertise. To bridge this knowledge gap, Maxime Labonne, a leading AI ...
#LLM #ArtificialIntelligence #MachineLearning #DeepLearning #AIEngineering #LargeLanguageModels
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π€π§ LangChain: The Ultimate Framework for Building Reliable AI Agents and LLM Applications
ποΈ 24 Oct 2025
π AI News & Trends
As artificial intelligence continues to transform industries, developers are racing to build smarter, more adaptive applications powered by Large Language Models (LLMs). Yet, one major challenge remains how to make these models interact intelligently with real-world data and external systems in a scalable, reliable way. Enter LangChain, an open-source framework designed to make LLM-powered application ...
#LangChain #AI #LLM #ArtificialIntelligence #OpenSource #AIAgents
ποΈ 24 Oct 2025
π AI News & Trends
As artificial intelligence continues to transform industries, developers are racing to build smarter, more adaptive applications powered by Large Language Models (LLMs). Yet, one major challenge remains how to make these models interact intelligently with real-world data and external systems in a scalable, reliable way. Enter LangChain, an open-source framework designed to make LLM-powered application ...
#LangChain #AI #LLM #ArtificialIntelligence #OpenSource #AIAgents
β€5π2
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This combination is perhaps as low as we can get to explain how the Transformer works
#Transformers #LLM #AI
https://xn--r1a.website/CodeProgrammer π
#Transformers #LLM #AI
https://xn--r1a.website/CodeProgrammer π
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Forwarded from Machine Learning
100+ LLM Interview Questions and Answers (GitHub Repo)
Anyone preparing for #AI/#ML Interviews, it is mandatory to have good knowledge related to #LLM topics.
This# repo includes 100+ LLM interview questions (with answers) spanning over LLM topics like
LLM Inference
LLM Fine-Tuning
LLM Architectures
LLM Pretraining
Prompt Engineering
etc.
π Github Repo - https://github.com/KalyanKS-NLP/LLM-Interview-Questions-and-Answers-Hub
https://xn--r1a.website/DataScienceMβ
Anyone preparing for #AI/#ML Interviews, it is mandatory to have good knowledge related to #LLM topics.
This# repo includes 100+ LLM interview questions (with answers) spanning over LLM topics like
LLM Inference
LLM Fine-Tuning
LLM Architectures
LLM Pretraining
Prompt Engineering
etc.
https://xn--r1a.website/DataScienceM
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Machine Learning Roadmap 2026
#MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #DataAnalysis #LLM #python
https://xn--r1a.website/CodeProgrammer
#MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #DataAnalysis #LLM #python
https://xn--r1a.website/CodeProgrammer
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DS Interview.pdf
1.6 MB
Data Science Interview questions
#DeepLearning #AI #MachineLearning #NeuralNetworks #DataScience #DataAnalysis #LLM #InterviewQuestions
https://xn--r1a.website/CodeProgrammer
#DeepLearning #AI #MachineLearning #NeuralNetworks #DataScience #DataAnalysis #LLM #InterviewQuestions
https://xn--r1a.website/CodeProgrammer
β€10π2π₯2
π Building our own mini-Skynet β a collection of 10 powerful AI repositories from big tech companies
1. Generative AI for Beginners and AI Agents for Beginners
Microsoft provides a detailed explanation of generative AI and agent architecture: from theory to practice.
2. LLMs from Scratch
Step-by-step assembly of your own GPT to understand how LLMs are structured "under the hood".
3. OpenAI Cookbook
An official set of examples for working with APIs, RAG systems, and integrating AI into production from OpenAI.
4. Segment Anything and Stable Diffusion
Classic tools for computer vision and image generation from Meta and the CompVis research team.
5. Python 100 Days and Python Data Science Handbook
A powerful resource for Python and data analysis.
6. LLM App Templates and ML for Beginners
Ready-made app templates with LLMs and a structured course on classic machine learning.
If you want to delve deeply into AI or start building your own projects β this is an excellent starting kit.
tags: #github #LLM #AI #ML
β‘οΈ https://xn--r1a.website/CodeProgrammer
1. Generative AI for Beginners and AI Agents for Beginners
Microsoft provides a detailed explanation of generative AI and agent architecture: from theory to practice.
2. LLMs from Scratch
Step-by-step assembly of your own GPT to understand how LLMs are structured "under the hood".
3. OpenAI Cookbook
An official set of examples for working with APIs, RAG systems, and integrating AI into production from OpenAI.
4. Segment Anything and Stable Diffusion
Classic tools for computer vision and image generation from Meta and the CompVis research team.
5. Python 100 Days and Python Data Science Handbook
A powerful resource for Python and data analysis.
6. LLM App Templates and ML for Beginners
Ready-made app templates with LLMs and a structured course on classic machine learning.
If you want to delve deeply into AI or start building your own projects β this is an excellent starting kit.
tags: #github #LLM #AI #ML
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A rather insightful ML roadmap has gone viral on GitHub: within it, the author has compiled a path from a foundation in mathematics, NumPy, and Pandas to LLM, agentic RAG, fine-tuning, MLOps, and interview preparation. The repository indeed includes sections on Karpathy, MCP, RLHF, LoRA/PEFT, and system design for AI interviews.
Conveniently, this isn't just a list of random links, but rather a structured route through the topics:
https://github.com/loganthorneloe/ml-roadmap
tags: #ml #llm
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Interactive Explainer π§ β¨
The Anatomy of an LLM π
A visual walk through the machinery inside a large language model: from raw text, to tokens, to vectors, to attention, to the next token. βοΈπ§¬
π Link: https://www.royvanrijn.com/anatomy-of-an-llm/
#LLM #AI #Tech #NeuralNetworks #MachineLearning #DeepLearning
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The Anatomy of an LLM π
A visual walk through the machinery inside a large language model: from raw text, to tokens, to vectors, to attention, to the next token. βοΈπ§¬
π Link: https://www.royvanrijn.com/anatomy-of-an-llm/
#LLM #AI #Tech #NeuralNetworks #MachineLearning #DeepLearning
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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.
β€10
Forwarded from Data Analytics
Transformers & LLMs Cheatsheet.pdf
1.4 MB
The only LLM cheat sheet you'll ever need π
Covers the main concepts, architectures, and practical applications.
### Basics
- Tokens (tokenization, BPE)
- Embeddings (cosine similarity)
- Attention mechanism (Attention formula, Multi-Head Attention)
### Transformer architecture and its variants
- BERT (models with only an encoder)
- GPT (models with only a decoder)
- T5 (models with an encoder and a decoder)
### Large language models (LLMs)
- Prompting (context length, Chain-of-Thought)
- Pre-training (SFT, PEFT/LoRA)
- Preference tuning (Reward Model, Reinforcement Learning)
- Optimizations (Mixture of Experts, Distillation, Quantization)
### Applications
- LLM-as-a-Judge (LaaJ)
- RAG (Retrieval-Augmented Generation)
- Agents (ReAct)
- Reasoning models (Scaling)
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#LLM #AI #MachineLearning #DeepLearning #PromptEngineering #Tech
Covers the main concepts, architectures, and practical applications.
### Basics
- Tokens (tokenization, BPE)
- Embeddings (cosine similarity)
- Attention mechanism (Attention formula, Multi-Head Attention)
### Transformer architecture and its variants
- BERT (models with only an encoder)
- GPT (models with only a decoder)
- T5 (models with an encoder and a decoder)
### Large language models (LLMs)
- Prompting (context length, Chain-of-Thought)
- Pre-training (SFT, PEFT/LoRA)
- Preference tuning (Reward Model, Reinforcement Learning)
- Optimizations (Mixture of Experts, Distillation, Quantization)
### Applications
- LLM-as-a-Judge (LaaJ)
- RAG (Retrieval-Augmented Generation)
- Agents (ReAct)
- Reasoning models (Scaling)
β¨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
βοΈ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
#LLM #AI #MachineLearning #DeepLearning #PromptEngineering #Tech
β€6π1
Forwarded from Data Analytics
The ultimate guide to fine tuning.pdf
15.2 MB
π The Big Book on Fine-Tuning LLMs
A free 115-page book dedicated to the retraining of large language models. π
It's suitable for those who want to understand how to prepare datasets, configure training, and improve the quality of LLMs for their tasks. π
#LLM #FineTuning #AI #MachineLearning #DataScience #Tech
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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
A free 115-page book dedicated to the retraining of large language models. π
It's suitable for those who want to understand how to prepare datasets, configure training, and improve the quality of LLMs for their tasks. π
#LLM #FineTuning #AI #MachineLearning #DataScience #Tech
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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
β€5π4
Data Science Interview Questions.pdf
1.4 MB
Data Science Interview Questions
π‘ Here is your curated list for Data Science interviews!
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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
#DataScience #AI #MachineLearning #LLM #TechJobs #InterviewPrep
π‘ Here is your curated list for Data Science interviews!
β¨ Join Best TG Channels https://xn--r1a.website/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
#DataScience #AI #MachineLearning #LLM #TechJobs #InterviewPrep
β€2π2π1
5 Fun Papers That Explain LLMs Clearly πβ¨
Want to understand LLMs better? Start with these five foundational papers that explain how they work. π€
Large language models (LLMs) can feel complicated at first. There are transformers, attention layers, scaling laws, pretraining, instruction tuning, human feedback, retrieval, and many other ideas around them. π§ But the best way to understand large language models is not to start with a huge textbook. A better way is to read a few important papers that each explain one major part of the system. π This article is part of a fun series where we learn by exploring core ideas, practical projects, and the research papers behind modern technology. π¬ In this article, we will go through five papers that explain how LLMs work. So, let's get started. π
More: https://www.kdnuggets.com/5-fun-papers-that-explain-llms-clearly
#LLM #AI #MachineLearning #DeepLearning #DataScience #Tech
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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
Want to understand LLMs better? Start with these five foundational papers that explain how they work. π€
Large language models (LLMs) can feel complicated at first. There are transformers, attention layers, scaling laws, pretraining, instruction tuning, human feedback, retrieval, and many other ideas around them. π§ But the best way to understand large language models is not to start with a huge textbook. A better way is to read a few important papers that each explain one major part of the system. π This article is part of a fun series where we learn by exploring core ideas, practical projects, and the research papers behind modern technology. π¬ In this article, we will go through five papers that explain how LLMs work. So, let's get started. π
More: https://www.kdnuggets.com/5-fun-papers-that-explain-llms-clearly
#LLM #AI #MachineLearning #DeepLearning #DataScience #Tech
β¨ Join Best TG Channels https://xn--r1a.website/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€4
Agentic_Design_Patterns.pdf
19.2 MB
Agentic Design Patterns - a free 421-page document from a senior Google engineer. πβ¨
It's rare to find materials of such a large volume where the author doesn't try to sell a course after every chapter. π‘
Inside:
⬩ agent architectures
⬩ multi-agent systems
⬩ memory and context management
⬩ orchestration and task planning
⬩ tools, MCP, and integrations
⬩ production cases and code examples
#AgenticAI #LLM #GoogleEngineering #MultiAgentSystems #AIDevelopment #TechDocs
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It's rare to find materials of such a large volume where the author doesn't try to sell a course after every chapter. π‘
Inside:
⬩ agent architectures
⬩ multi-agent systems
⬩ memory and context management
⬩ orchestration and task planning
⬩ tools, MCP, and integrations
⬩ production cases and code examples
#AgenticAI #LLM #GoogleEngineering #MultiAgentSystems #AIDevelopment #TechDocs
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