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I found the BEST video explaining how LLMs work π
π check out the full video here : https://lnkd.in/dvjZS89d
#LLM #ML #AI #Python
By: https://xn--r1a.website/DataAnalyticsX
π check out the full video here : https://lnkd.in/dvjZS89d
#LLM #ML #AI #Python
By: https://xn--r1a.website/DataAnalyticsX
β€2π2
LLM Interview Questions.pdf
71.2 KB
π 50 interview questions for LLM
A good warm-up before the interview: 50 questions on Large Language Models in one document. Not in-depth, but as a checklist to test your knowledge β just perfect.
tags: #LLM #ML #python #pytorch
β‘ https://xn--r1a.website/DataAnalyticsX
A good warm-up before the interview: 50 questions on Large Language Models in one document. Not in-depth, but as a checklist to test your knowledge β just perfect.
tags: #LLM #ML #python #pytorch
β‘ https://xn--r1a.website/DataAnalyticsX
π₯3β€2
Forwarded from Machine Learning
They cover the entire spectrum: classic ML, LLM, and generative models β with theory and practice.
tags: #python #ML #LLM #AI
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β€6
LLMs are the new operating system for work. ππ»
But most people still donβt know the difference between RAG, Embeddings, and Hallucinations. π€π§
Hereβs the vocabulary cheat sheet everyone in AI should know πβ¨
These foundational LLM concepts every professional, creator, founder, and tech enthusiast should know π©βπΌπ¨βπ»π¨π
#LLM #DataScience #AI #ML
https://xn--r1a.website/DataAnalyticsXπ
But most people still donβt know the difference between RAG, Embeddings, and Hallucinations. π€π§
Hereβs the vocabulary cheat sheet everyone in AI should know πβ¨
These foundational LLM concepts every professional, creator, founder, and tech enthusiast should know π©βπΌπ¨βπ»π¨π
#LLM #DataScience #AI #ML
https://xn--r1a.website/DataAnalyticsX
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β€4π1
π Collecting free tokens from all LLM providers in one project π€β¨
The developer has created an open-source tool: you add API keys from platforms with free limits. ππ»
The system automatically switches between them when one runs out. ππ
βοΈ Link to GitHub
https://github.com/tashfeenahmed/freellmapi
#LLM #FreeTokens #OpenSource #AI #Developer #Tech
The developer has created an open-source tool: you add API keys from platforms with free limits. ππ»
The system automatically switches between them when one runs out. ππ
βοΈ Link to GitHub
https://github.com/tashfeenahmed/freellmapi
#LLM #FreeTokens #OpenSource #AI #Developer #Tech
β€6
β‘οΈ Machine Learning Roadmap 2026: a large map for entering ML without fairy tales about "neural networks in a month" π€
A large Russian-language roadmap for machine learning: from the first import of numpy to LLM, RAG, fine-tuning, AI agents, and MLOps, and even Vue coding. π
Inside, there's a normal structure: what to learn, in what order, why it's needed, and what should be achieved in practice after each stage. π§
The roadmap is divided into 7 tracks: π
1. Foundation: Python, mathematics, statistics, tools ποΈ
2. Classic ML: scikit-learn, tabular data, metrics, validation π
3. Deep Learning: PyTorch, CNN, RNN, training loop π§
4. LLM and transformers: attention, KV-cache, RAG, LoRA, agents π€
5. Generative AI: images, videos, audio, multimodality π¨
6. MLOps and production: Docker, Kubernetes, CI/CD, monitoring, serving βοΈ
7. Specialization: CV, NLP, RecSys, RL, Safety π―
The roadmap doesn't sell the illusion of "training a model - becoming an ML engineer". π«
In real work, a lot of time is spent on data, metrics, deployment, monitoring, reproducibility, and error analysis. Model is just part of the system. π οΈ
A good idea from the roadmap: LLM doesn't make a junior a senior. It accelerates someone who already understands the basics. Without the basics, a person just becomes an operator of Copilot, who can't explain why everything broke down. π
In terms of time, it's no fairy tale either: β³
1. 0-3 months: mathematics, classic ML π
2. 3-6 months: Deep Learning and PyTorch π₯
3. 6-12 months: LLM, RAG, fine-tuning, AI agents π€
4. 12+ months: MLOps, production, scaling, specialization π
Here, seven large free courses on machine learning, mathematics, and Vue coding are also collected! π
If you've long wanted to enter ML systematically, rather than jumping between videos about ChatGPT, Stable Diffusion, and "top-10 libraries", this is a good guide. πΊοΈ
https://github.com/justxor/MachineLearningRoadmap π
#MachineLearning #AI #DataScience #LLM #MLOps #Python
A large Russian-language roadmap for machine learning: from the first import of numpy to LLM, RAG, fine-tuning, AI agents, and MLOps, and even Vue coding. π
Inside, there's a normal structure: what to learn, in what order, why it's needed, and what should be achieved in practice after each stage. π§
The roadmap is divided into 7 tracks: π
1. Foundation: Python, mathematics, statistics, tools ποΈ
2. Classic ML: scikit-learn, tabular data, metrics, validation π
3. Deep Learning: PyTorch, CNN, RNN, training loop π§
4. LLM and transformers: attention, KV-cache, RAG, LoRA, agents π€
5. Generative AI: images, videos, audio, multimodality π¨
6. MLOps and production: Docker, Kubernetes, CI/CD, monitoring, serving βοΈ
7. Specialization: CV, NLP, RecSys, RL, Safety π―
The roadmap doesn't sell the illusion of "training a model - becoming an ML engineer". π«
In real work, a lot of time is spent on data, metrics, deployment, monitoring, reproducibility, and error analysis. Model is just part of the system. π οΈ
A good idea from the roadmap: LLM doesn't make a junior a senior. It accelerates someone who already understands the basics. Without the basics, a person just becomes an operator of Copilot, who can't explain why everything broke down. π
In terms of time, it's no fairy tale either: β³
1. 0-3 months: mathematics, classic ML π
2. 3-6 months: Deep Learning and PyTorch π₯
3. 6-12 months: LLM, RAG, fine-tuning, AI agents π€
4. 12+ months: MLOps, production, scaling, specialization π
Here, seven large free courses on machine learning, mathematics, and Vue coding are also collected! π
If you've long wanted to enter ML systematically, rather than jumping between videos about ChatGPT, Stable Diffusion, and "top-10 libraries", this is a good guide. πΊοΈ
https://github.com/justxor/MachineLearningRoadmap π
#MachineLearning #AI #DataScience #LLM #MLOps #Python
GitHub
GitHub - justxor/MachineLearningRoadmap: ΠΠΎΠ»Π½ΡΠΉ Roadmap ΠΏΠΎ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠΌΡ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ 2026
ΠΠΎΠ»Π½ΡΠΉ Roadmap ΠΏΠΎ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠΌΡ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ 2026 . Contribute to justxor/MachineLearningRoadmap development by creating an account on GitHub.
β€3
Assembling GPT-like LLMs from scratch on PyTorch π₯
https://github.com/analyticalrohit/llms-from-scratch
π 10 notebooks. Step-by-step explanation.
𧩠Breaks down the architecture of LLMs into simple parts.
β Suitable for beginners.
π Completely hands-on.
#PyTorch #LLM #AI #MachineLearning #DeepLearning #Code
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βοΈ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
https://github.com/analyticalrohit/llms-from-scratch
π 10 notebooks. Step-by-step explanation.
𧩠Breaks down the architecture of LLMs into simple parts.
β Suitable for beginners.
π Completely hands-on.
#PyTorch #LLM #AI #MachineLearning #DeepLearning #Code
β¨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
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β€4
π Create an LLM from Scratch!
I came across a great find from Vizuara β a series of 43 lectures that truly delivers on its promise: showing how to build a large language model from scratch. π§ β¨
Most people use ChatGPT.
But only a few actually understand how it works under the hood. βοΈ
This playlist step by step breaks down all the key concepts without overloading with complex explanations.
π What you will learn:
β The architecture of Transformer ποΈ
β The internal structure of GPT
β Tokenization and BPE π§©
β Attention mechanisms π
β The process of training an LLM π
β Full implementations in Python π
β Suitable for:
β’ ML engineers
β’ AI enthusiasts
β’ Developers entering the GenAI field
β’ Anyone who is tired of explaining AI as a "black box" π΅οΈ
If you really want to understand what lies at the heart of models like ChatGPT, Claude, and Gemini β this material is worth watching. π
π Link to the playlist:
https://www.youtube.com/playlist?list=PLPTV0NXA_ZSgsLAr8YCgCwhPIJNNtexWu
#LLM #AI #MachineLearning #Python #GenAI #DeepLearning
β¨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
βοΈ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
I came across a great find from Vizuara β a series of 43 lectures that truly delivers on its promise: showing how to build a large language model from scratch. π§ β¨
Most people use ChatGPT.
But only a few actually understand how it works under the hood. βοΈ
This playlist step by step breaks down all the key concepts without overloading with complex explanations.
π What you will learn:
β The architecture of Transformer ποΈ
β The internal structure of GPT
β Tokenization and BPE π§©
β Attention mechanisms π
β The process of training an LLM π
β Full implementations in Python π
β Suitable for:
β’ ML engineers
β’ AI enthusiasts
β’ Developers entering the GenAI field
β’ Anyone who is tired of explaining AI as a "black box" π΅οΈ
If you really want to understand what lies at the heart of models like ChatGPT, Claude, and Gemini β this material is worth watching. π
π Link to the playlist:
https://www.youtube.com/playlist?list=PLPTV0NXA_ZSgsLAr8YCgCwhPIJNNtexWu
#LLM #AI #MachineLearning #Python #GenAI #DeepLearning
β¨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
βοΈ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
β€5
π Found a huge database on System Design for GenAI and LLM! π€π
500+ real reviews of GenAI, LLM, and ML systems from OpenAI, Anthropic, Google, Microsoft, Netflix, and dozens of other companies. ππ’
A real find for those who are building AI products or want to understand how market leaders do it. ππ‘
βοΈ Link to GitHub
https://github.com/themanojdesai/genai-llm-ml-case-studies
#SystemDesign #GenAI #LLM #MachineLearning #AI #Tech
β¨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
βοΈ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
500+ real reviews of GenAI, LLM, and ML systems from OpenAI, Anthropic, Google, Microsoft, Netflix, and dozens of other companies. ππ’
A real find for those who are building AI products or want to understand how market leaders do it. ππ‘
βοΈ Link to GitHub
https://github.com/themanojdesai/genai-llm-ml-case-studies
#SystemDesign #GenAI #LLM #MachineLearning #AI #Tech
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β€5
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€6
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
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€2
π LLM Scraper β parsing websites through neural networks
The tool allows you to convert any web pages into structured data using LLM.
Useful for data collection, site monitoring, and preparing datasets without writing complex parsers.
βοΈ Link to GitHub: https://github.com/mishushakov/llm-scraper
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
#LLM #Scraper #WebScraping #DataCollection #AI #Automation
The tool allows you to convert any web pages into structured data using LLM.
Useful for data collection, site monitoring, and preparing datasets without writing complex parsers.
βοΈ Link to GitHub: https://github.com/mishushakov/llm-scraper
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
#LLM #Scraper #WebScraping #DataCollection #AI #Automation
β€4
Stop studying LLM from random articles and videos that only explain individual pieces of the puzzle.
π LLM from Scratch β this is a practical course on PyTorch for those who want to understand the entire path of modern LLMs: from the first Transformer block to RLHF.
Instead of endless theory, here we gather a complete model training chain:
πΉ Pretraining β Finetuning β Alignment in one course
πΉ Transformer from scratch: positional embeddings, self-attention, multi-head attention, MLP, residual connections, LayerNorm, and full Transformer blocks
πΉ Own training loop without Trainer magic: tokenization, batches, cross-entropy, validation loss, text generation
πΉ Modern architecture improvements: RMSNorm, RoPE, SwiGLU, KV Cache, sliding-window attention, and streaming cache
πΉ Full section on alignment: SFT, reward models, PPO-style RLHF, and GRPO with an analysis of how it looks in the training loop in practice
https://github.com/vivekkalyanarangan30/llm_from_scratch
#LLM #PyTorch #MachineLearning #DeepLearning #AI #Transformer
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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 LLM from Scratch β this is a practical course on PyTorch for those who want to understand the entire path of modern LLMs: from the first Transformer block to RLHF.
Instead of endless theory, here we gather a complete model training chain:
πΉ Pretraining β Finetuning β Alignment in one course
πΉ Transformer from scratch: positional embeddings, self-attention, multi-head attention, MLP, residual connections, LayerNorm, and full Transformer blocks
πΉ Own training loop without Trainer magic: tokenization, batches, cross-entropy, validation loss, text generation
πΉ Modern architecture improvements: RMSNorm, RoPE, SwiGLU, KV Cache, sliding-window attention, and streaming cache
πΉ Full section on alignment: SFT, reward models, PPO-style RLHF, and GRPO with an analysis of how it looks in the training loop in practice
https://github.com/vivekkalyanarangan30/llm_from_scratch
#LLM #PyTorch #MachineLearning #DeepLearning #AI #Transformer
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€2
Forwarded from Machine Learning with Python
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
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
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
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€5
A large collection of materials on LLM Systems,
β’ model training (pre-training, RLHF, fault tolerance, stragglers)
β’ inference and serving
β’ agent systems
β’ edge deployment
β’ multimodal models
β’ technical reports from major laboratories
β’ reviews, benchmarks, and leaderboards
β’ courses on MLSys and collections of articles from conferences
https://github.com/AmberLJC/LLMSys-PaperList
#LLMSys #LLM #MachineLearning #AIResearch #DeepLearning #TechReports
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’ model training (pre-training, RLHF, fault tolerance, stragglers)
β’ inference and serving
β’ agent systems
β’ edge deployment
β’ multimodal models
β’ technical reports from major laboratories
β’ reviews, benchmarks, and leaderboards
β’ courses on MLSys and collections of articles from conferences
https://github.com/AmberLJC/LLMSys-PaperList
#LLMSys #LLM #MachineLearning #AIResearch #DeepLearning #TechReports
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
GitHub
GitHub - AmberLJC/LLMSys-PaperList: Large Language Model (LLM) Systems Paper List
Large Language Model (LLM) Systems Paper List. Contribute to AmberLJC/LLMSys-PaperList development by creating an account on GitHub.
β€1
Forwarded from Machine Learning with Python
10 GitHub repositories that are worth checking out for an AI engineer π€
1. Hands-On AI Engineering π οΈ
A collection of AI applications and agent systems with practical use cases of LLM.
π https://github.com/Sumanth077/Hands-On-AI-Engineering
2. Hands-On Large Language Models π
Full code from the book Hands-On Large Language Models: from basics to fine-tuning.
π https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
3. AI Agents for Beginners π
A free course from Microsoft with 11 lessons on creating AI agents.
π https://github.com/microsoft/ai-agents-for-beginners
4. GenAI Agents π€
A large collection of tutorials and implementations of agent systems.
π https://github.com/NirDiamant/GenAI_Agents
5. Made With ML π
About the development, deployment, and support of production-ready ML systems.
π https://github.com/GokuMohandas/Made-With-ML
6. Learn Harness Engineering βοΈ
A practical course on Harness Engineering for AI agents.
π https://github.com/walkinglabs/learn-harness-engineering
7. AutoResearch π¬
Autonomous cycles of ML experiments from Andrej Karpathy.
π https://github.com/karpathy/autoresearch
8. Designing Machine Learning Systems π
Notes and materials from Chip Huyen's book.
π https://github.com/chiphuyen/dmls-book
9. Awesome LLM Inference β‘
A collection of materials on LLM inference: Flash Attention, KV Cache, quantization, and more.
π https://github.com/xlite-dev/Awesome-LLM-Inference
10. LLM Course πΊοΈ
A practical course on LLM with a roadmap and Colab notebooks.
π https://github.com/mlabonne/llm-course
#AI #MachineLearning #LLM #DataScience #Tech #GitHub
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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
1. Hands-On AI Engineering π οΈ
A collection of AI applications and agent systems with practical use cases of LLM.
π https://github.com/Sumanth077/Hands-On-AI-Engineering
2. Hands-On Large Language Models π
Full code from the book Hands-On Large Language Models: from basics to fine-tuning.
π https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
3. AI Agents for Beginners π
A free course from Microsoft with 11 lessons on creating AI agents.
π https://github.com/microsoft/ai-agents-for-beginners
4. GenAI Agents π€
A large collection of tutorials and implementations of agent systems.
π https://github.com/NirDiamant/GenAI_Agents
5. Made With ML π
About the development, deployment, and support of production-ready ML systems.
π https://github.com/GokuMohandas/Made-With-ML
6. Learn Harness Engineering βοΈ
A practical course on Harness Engineering for AI agents.
π https://github.com/walkinglabs/learn-harness-engineering
7. AutoResearch π¬
Autonomous cycles of ML experiments from Andrej Karpathy.
π https://github.com/karpathy/autoresearch
8. Designing Machine Learning Systems π
Notes and materials from Chip Huyen's book.
π https://github.com/chiphuyen/dmls-book
9. Awesome LLM Inference β‘
A collection of materials on LLM inference: Flash Attention, KV Cache, quantization, and more.
π https://github.com/xlite-dev/Awesome-LLM-Inference
10. LLM Course πΊοΈ
A practical course on LLM with a roadmap and Colab notebooks.
π https://github.com/mlabonne/llm-course
#AI #MachineLearning #LLM #DataScience #Tech #GitHub
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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
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π Building our own GPT-like model in PyTorch
We've found an excellent repository for those who want to understand how modern LLMs are built under the hood.
Inside β 10 Jupyter notebooks with step-by-step explanations and implementations of key components of language models.
GitHub: https://github.com/analyticalrohit/llms-from-scratch
#PyTorch #LLM #MachineLearning #AI #DeepLearning #DataScience
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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've found an excellent repository for those who want to understand how modern LLMs are built under the hood.
Inside β 10 Jupyter notebooks with step-by-step explanations and implementations of key components of language models.
GitHub: https://github.com/analyticalrohit/llms-from-scratch
#PyTorch #LLM #MachineLearning #AI #DeepLearning #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
β€3
Curates LLM tools and research for scientific discovery π§¬π¬
Repo: https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Discovery ππ
#LLM #ScientificDiscovery #ResearchTools #AI #MachineLearning #DataScience
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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
Repo: https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Discovery ππ
#LLM #ScientificDiscovery #ResearchTools #AI #MachineLearning #DataScience
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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€1
π How to Reduce the Cost of LLM Inference by up to 90%
If your AI agents are constantly sending the same context, consider using LMCache. π
This open-source system manages a KV cache, allowing you to reuse already computed representations instead of recalculating them for each request. π§
As a result:
β‘οΈ Up to 14x faster Time To First Token;
β‘οΈ Up to 4x faster decoding;
β‘οΈ Significant savings in GPU resources and inference costs. π°
βοΈ Link to GitHub
https://github.com/LMCache/LMCache
#LLM #AIOptimization #LMCache #GPU #CostReduction #AIEngineering
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If your AI agents are constantly sending the same context, consider using LMCache. π
This open-source system manages a KV cache, allowing you to reuse already computed representations instead of recalculating them for each request. π§
As a result:
β‘οΈ Up to 14x faster Time To First Token;
β‘οΈ Up to 4x faster decoding;
β‘οΈ Significant savings in GPU resources and inference costs. π°
βοΈ Link to GitHub
https://github.com/LMCache/LMCache
#LLM #AIOptimization #LMCache #GPU #CostReduction #AIEngineering
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β€3
π π€ Run Your Own LOCAL LLM (Beginner Friendly)
LLMs are cool, but running your own local one hits different π
No cloud. No API keys. No limits.
𧩠Step 1: Install Ollama
Install Ollama on your machine (works on Mac, Windows, Linux).
Once installed, open your terminal.
π Step 2: Run a model
This command:
β’ Downloads the model
β’ Starts it locally
β’ Lets you chat instantly π¬
If you see the prompt, your local LLM is running.
βοΈ Step 3: Do local inference (API style)
Ollama runs a local server on your machine.
If you get a JSON response with text β β it works.
π‘ Why this is powerful
β’ Works offline
β’ Private by default
β’ Perfect for learning, testing, and small apps
This is the easiest way to start with LLMs locally.
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#LLM #AI #LocalLLM #Ollama #Tech #Linux
LLMs are cool, but running your own local one hits different π
No cloud. No API keys. No limits.
𧩠Step 1: Install Ollama
Install Ollama on your machine (works on Mac, Windows, Linux).
Once installed, open your terminal.
π Step 2: Run a model
ollama run llama3.2
This command:
β’ Downloads the model
β’ Starts it locally
β’ Lets you chat instantly π¬
If you see the prompt, your local LLM is running.
βοΈ Step 3: Do local inference (API style)
Ollama runs a local server on your machine.
curl http://127.0.0.1:11434/api/generate \
-H "Content-Type: application/json" \
-d '{
"model": "llama3.2",
"prompt": "Explain overfitting like I am 12",
"stream": false
}'
If you get a JSON response with text β β it works.
π‘ Why this is powerful
β’ Works offline
β’ Private by default
β’ Perfect for learning, testing, and small apps
This is the easiest way to start with LLMs locally.
β¨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
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#LLM #AI #LocalLLM #Ollama #Tech #Linux
β€4