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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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โค15๐2
Rocket.new lets you build a full website using prompts with their vibe solutioning platform ๐ง โก๏ธ
You describe it, it does the work.
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Go to Rocket.new now, enter the code, claim your 2 months free, or miss out and come back later paying the full subscription.๐ธ
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You describe it, it does the work.
X7K2M9P4R1NQGo to Rocket.new now, enter the code, claim your 2 months free, or miss out and come back later paying the full subscription.
claim your 2 months free
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Forwarded from Learn Python Hub
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Python Tip: Operator Overloading
This is a very important concept in Python.
๐ https://t.me/Python53
This is a very important concept in Python.
Have you ever wondered how #Python understands what the + operator means? For numbers, it's addition; for strings, it's concatenation; for lists, it's union. This is operator overloading in action.๏ปฟ
Operator overloading means defining special behavior for operators (+, -, *, ==, etc.) in your user-defined classes. You determine how these operators should work with your objects.
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Horizon Lab ๐ญ ะะถะตะนะผั ะะตะฑะฑ ะทะฝะฐั
ะพะดะธัั ะณะฐะปะฐะบัะธะบะธ, ัะบะธั
ะฝะต ะผะฐะปะพ ะฑ ััะฝัะฒะฐัะธ ะทะฐ ะฝะฐัะธะผะธ ะผะพะดะตะปัะผะธ. Hubble ะฑะฐัะธัั ะทััะบะธ, ัะพ ะฒะธะฑัั
ะฝัะปะธ ะผัะปัััะดะธ ัะพะบัะฒ ัะพะผั. ะะธัะตะผะพ ะฟัะพ ัะต ัะพะดะฝั โ ัะบัะฐัะฝััะบะพั, ะฝะฐ ะพัะฝะพะฒั ะฝะฐัะบะพะฒะธั
ะฟัะฑะปัะบะฐััะน.
๐ http://t.me/horizonlab_space
๐ http://t.me/horizonlab_space
โค7๐ฅ1
TOP RAG INTERVIEW.pdf
166 KB
๐ ๐๐๐ ๐๐๐ ๐๐๐๐๐๐๐๐๐ ๐๐๐๐๐๐๐๐๐ ๐๐๐ ๐๐๐๐๐๐๐ โฃโฃ
๐น Advanced #RAG engineering conceptsโฃโฃ
โข Multi-stage retrieval pipelinesโฃโฃ
โข Agentic RAG vs classical RAGโฃโฃ
โข Latency optimizationโฃโฃ
โข Security risks in enterprise RAG systemsโฃโฃ
โข Monitoring and debugging production RAG systemsโฃโฃ
โฃโฃ
๐ ๐๐ก๐ ๐๐๐ ๐๐จ๐ง๐ญ๐๐ข๐ง๐ฌ ๐๐ ๐ฌ๐ญ๐ซ๐ฎ๐๐ญ๐ฎ๐ซ๐๐ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ ๐ฐ๐ข๐ญ๐ก ๐๐ฅ๐๐๐ซ ๐๐ฑ๐ฉ๐ฅ๐๐ง๐๐ญ๐ข๐จ๐ง๐ฌ ๐ญ๐จ ๐ก๐๐ฅ๐ฉ ๐ฒ๐จ๐ฎ ๐ฎ๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐ ๐๐จ๐ญ๐ก ๐๐จ๐ง๐๐๐ฉ๐ญ๐ฌ ๐๐ง๐ ๐ฌ๐ฒ๐ฌ๐ญ๐๐ฆ ๐๐๐ฌ๐ข๐ ๐ง ๐ญ๐ก๐ข๐ง๐ค๐ข๐ง๐ .โฃโฃ
โฃโฃ
https://t.me/CodeProgrammer
๐น Advanced #RAG engineering conceptsโฃโฃ
โข Multi-stage retrieval pipelinesโฃโฃ
โข Agentic RAG vs classical RAGโฃโฃ
โข Latency optimizationโฃโฃ
โข Security risks in enterprise RAG systemsโฃโฃ
โข Monitoring and debugging production RAG systemsโฃโฃ
โฃโฃ
๐ ๐๐ก๐ ๐๐๐ ๐๐จ๐ง๐ญ๐๐ข๐ง๐ฌ ๐๐ ๐ฌ๐ญ๐ซ๐ฎ๐๐ญ๐ฎ๐ซ๐๐ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ ๐ฐ๐ข๐ญ๐ก ๐๐ฅ๐๐๐ซ ๐๐ฑ๐ฉ๐ฅ๐๐ง๐๐ญ๐ข๐จ๐ง๐ฌ ๐ญ๐จ ๐ก๐๐ฅ๐ฉ ๐ฒ๐จ๐ฎ ๐ฎ๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐ ๐๐จ๐ญ๐ก ๐๐จ๐ง๐๐๐ฉ๐ญ๐ฌ ๐๐ง๐ ๐ฌ๐ฒ๐ฌ๐ญ๐๐ฆ ๐๐๐ฌ๐ข๐ ๐ง ๐ญ๐ก๐ข๐ง๐ค๐ข๐ง๐ .โฃโฃ
โฃโฃ
https://t.me/CodeProgrammer
โค8
How a CNN sees images simplified ๐ง
1. Input โ Image breaks into pixels (RGB numbers)
2. Feature Extraction
ยท Convolution โ Detects edges/patterns
ยท ReLU โ Kills negatives, adds non-linearity
ยท Pooling โ Shrinks data, keeps what matters
3. Fully Connected โ Flattens features into meaning
4. Output โ Probability scores: Cat? Dog? Car?
Why powerful: Learns hierarchically โ edges โ shapes โ objects
Pixels to predictions. That's it. ๐
#DeepLearning #CNN #ComputerVision #AI
https://t.me/CodeProgrammer
1. Input โ Image breaks into pixels (RGB numbers)
2. Feature Extraction
ยท Convolution โ Detects edges/patterns
ยท ReLU โ Kills negatives, adds non-linearity
ยท Pooling โ Shrinks data, keeps what matters
3. Fully Connected โ Flattens features into meaning
4. Output โ Probability scores: Cat? Dog? Car?
Why powerful: Learns hierarchically โ edges โ shapes โ objects
Pixels to predictions. That's it. ๐
#DeepLearning #CNN #ComputerVision #AI
https://t.me/CodeProgrammer
โค9๐4
CNN vs Vision Transformer โ The Battle for Computer Vision ๐โก๏ธ
Two architectures. One goal: identify the cat. But they see things differently:
๐ง CNN (Convolutional Neural Network)
ยท Scans the image with filters
ยท Detects local patterns first (edges โ textures โ shapes)
ยท Builds understanding layer by layer
๐ Vision Transformer (ViT)
ยท Splits image into patches (like words in a sentence)
ยท Detects global patterns from the start
ยท Sees the whole picture using attention mechanisms
Same input. Same output. Different journey.
CNNs think locally and build up.
Transformers think globally from the get-go.
Which one wins? Depends on the task โ but both are shaping the future of how machines see.
https://t.me/CodeProgrammer
Two architectures. One goal: identify the cat. But they see things differently:
๐ง CNN (Convolutional Neural Network)
ยท Scans the image with filters
ยท Detects local patterns first (edges โ textures โ shapes)
ยท Builds understanding layer by layer
๐ Vision Transformer (ViT)
ยท Splits image into patches (like words in a sentence)
ยท Detects global patterns from the start
ยท Sees the whole picture using attention mechanisms
Same input. Same output. Different journey.
CNNs think locally and build up.
Transformers think globally from the get-go.
Which one wins? Depends on the task โ but both are shaping the future of how machines see.
https://t.me/CodeProgrammer
โค5๐1๐1
PhD Students - Do you need datasets for your research?
Here are 30 datasets for research from NexData.
Use discount code for 20% off: G5W924C3ZI
1. Korean Exam Question Dataset for AI Training
https://lnkd.in/d_paSwt7
2. Multilingual Grammar Correction Dataset
https://lnkd.in/dV43iqTp
3. High quality video caption dataset
https://lnkd.in/dY9kxkhx
4. 3D models and scenes datasets for AI and simulation
https://lnkd.in/dT-zscH4
5. Image editing datasets โ object removal, addition & modification
https://lnkd.in/dd8iCGMS
6. QA dataset โ visual & text reasoning
https://lnkd.in/dc3TNWFD
7. English instruction tuning dataset
https://lnkd.in/dTeTgd2M
8. Large scale vision language dataset for AI training
https://lnkd.in/dBJuxazN
9. News dataset
https://lnkd.in/dYBJe5gd
10. Global building photos dataset
https://lnkd.in/dVJsDXnC
11. Facial landmarks dataset
https://lnkd.in/dz_KGCS4
12. 3D Human Pose & Landmarks dataset
https://lnkd.in/dXE9ir8Z
13. 3D Hand Pose & Gesture Recognition dataset
https://lnkd.in/d_QdGGb9
14. 14. Driver monitoring dataset โ dangerous, fatigue
https://lnkd.in/d6kF-9PW
15. Japanese handwriting OCR dataset
https://lnkd.in/dHnriqrH
16. American English Male voice TTS dataset
https://lnkd.in/dqyvg862
17. Riddles and brain teasers dataset
https://lnkd.in/dKBHY3DE
18. Chinese test questions text
https://lnkd.in/dQpUd8xC
19. Chinese medical question answering data
https://lnkd.in/dsbWUCpz
20. Multi-round interpersonal dialogues text data
https://lnkd.in/dQiUq_Jg
21. Human activity recognition dataset
https://lnkd.in/dHM52MfV
22. Facial expression recognition dataset
https://lnkd.in/dqQAfMau
23. Urban surveillance dataset
https://lnkd.in/dc2RCnTk
24. Human body segmentation dataset
https://lnkd.in/d6sSrDxS
25. Fashion segmentation โ clothing & accessories
https://lnkd.in/dptNUTz8
26. Fight video dataset โ action recognition
https://lnkd.in/dnY_m5hZ
27. Gesture recognition dataset
https://lnkd.in/dFVPivYg
28. Facial skin defects dataset
https://lnkd.in/dKCbUvU6
29. Smoke detection and behaviour recognition dataset
https://lnkd.in/ddGg56R4
30. Weight loss transformation video dataset
https://lnkd.in/dqqT4ed9
https://t.me/CodeProgrammer๐พ
Here are 30 datasets for research from NexData.
Use discount code for 20% off: G5W924C3ZI
1. Korean Exam Question Dataset for AI Training
https://lnkd.in/d_paSwt7
2. Multilingual Grammar Correction Dataset
https://lnkd.in/dV43iqTp
3. High quality video caption dataset
https://lnkd.in/dY9kxkhx
4. 3D models and scenes datasets for AI and simulation
https://lnkd.in/dT-zscH4
5. Image editing datasets โ object removal, addition & modification
https://lnkd.in/dd8iCGMS
6. QA dataset โ visual & text reasoning
https://lnkd.in/dc3TNWFD
7. English instruction tuning dataset
https://lnkd.in/dTeTgd2M
8. Large scale vision language dataset for AI training
https://lnkd.in/dBJuxazN
9. News dataset
https://lnkd.in/dYBJe5gd
10. Global building photos dataset
https://lnkd.in/dVJsDXnC
11. Facial landmarks dataset
https://lnkd.in/dz_KGCS4
12. 3D Human Pose & Landmarks dataset
https://lnkd.in/dXE9ir8Z
13. 3D Hand Pose & Gesture Recognition dataset
https://lnkd.in/d_QdGGb9
14. 14. Driver monitoring dataset โ dangerous, fatigue
https://lnkd.in/d6kF-9PW
15. Japanese handwriting OCR dataset
https://lnkd.in/dHnriqrH
16. American English Male voice TTS dataset
https://lnkd.in/dqyvg862
17. Riddles and brain teasers dataset
https://lnkd.in/dKBHY3DE
18. Chinese test questions text
https://lnkd.in/dQpUd8xC
19. Chinese medical question answering data
https://lnkd.in/dsbWUCpz
20. Multi-round interpersonal dialogues text data
https://lnkd.in/dQiUq_Jg
21. Human activity recognition dataset
https://lnkd.in/dHM52MfV
22. Facial expression recognition dataset
https://lnkd.in/dqQAfMau
23. Urban surveillance dataset
https://lnkd.in/dc2RCnTk
24. Human body segmentation dataset
https://lnkd.in/d6sSrDxS
25. Fashion segmentation โ clothing & accessories
https://lnkd.in/dptNUTz8
26. Fight video dataset โ action recognition
https://lnkd.in/dnY_m5hZ
27. Gesture recognition dataset
https://lnkd.in/dFVPivYg
28. Facial skin defects dataset
https://lnkd.in/dKCbUvU6
29. Smoke detection and behaviour recognition dataset
https://lnkd.in/ddGg56R4
30. Weight loss transformation video dataset
https://lnkd.in/dqqT4ed9
https://t.me/CodeProgrammer
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๐ค Python libraries for AI agents โ what to study
If you want to develop AI agents in Python, it's important to understand the order of studying libraries.
Start with LangChain, CrewAI or SmolAgents โ they allow you to quickly assemble simple agents, connect tools, and test ideas.
The next level is LangGraph, LlamaIndex and Semantic Kernel. These tools are already used for production systems: RAG, orchestration, and complex workflows.
The most complex level is AutoGen, DSPy and A2A. They are needed for autonomous multi-agent systems and optimizing LLM pipelines.
LangChain โ simple agents, tools, and memory
github.com/langchain-ai/langchain
CrewAI โ multi-agent systems with roles
github.com/joaomdmoura/crewAI
SmolAgents โ lightweight agents for quick experiments
github.com/huggingface/smolagents
LangGraph โ orchestration and stateful workflow
github.com/langchain-ai/langgraph
LlamaIndex โ RAG and knowledge-agents
github.com/run-llama/llama_index
Semantic Kernel โ AI workflow and plugins
github.com/microsoft/semantic-kernel
AutoGen โ autonomous multi-agent systems
github.com/microsoft/autogen
DSPy โ optimizing LLM pipelines
github.com/stanfordnlp/dspy
A2A โ protocol for interaction between agents
github.com/a2aproject/A2A
https://t.me/CodeProgrammer๐
If you want to develop AI agents in Python, it's important to understand the order of studying libraries.
Start with LangChain, CrewAI or SmolAgents โ they allow you to quickly assemble simple agents, connect tools, and test ideas.
The next level is LangGraph, LlamaIndex and Semantic Kernel. These tools are already used for production systems: RAG, orchestration, and complex workflows.
The most complex level is AutoGen, DSPy and A2A. They are needed for autonomous multi-agent systems and optimizing LLM pipelines.
LangChain โ simple agents, tools, and memory
github.com/langchain-ai/langchain
CrewAI โ multi-agent systems with roles
github.com/joaomdmoura/crewAI
SmolAgents โ lightweight agents for quick experiments
github.com/huggingface/smolagents
LangGraph โ orchestration and stateful workflow
github.com/langchain-ai/langgraph
LlamaIndex โ RAG and knowledge-agents
github.com/run-llama/llama_index
Semantic Kernel โ AI workflow and plugins
github.com/microsoft/semantic-kernel
AutoGen โ autonomous multi-agent systems
github.com/microsoft/autogen
DSPy โ optimizing LLM pipelines
github.com/stanfordnlp/dspy
A2A โ protocol for interaction between agents
github.com/a2aproject/A2A
https://t.me/CodeProgrammer
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โค13๐ฅ1๐1
Forwarded from Machine Learning with Python
The Python + Generative AI series by Azure AI Foundry has ended, but all materials are open
Now you can calmly rewatch the recordings, download the slides, and try the code from each session โ from LLM and RAG to AI agents and MCP.
All resources are here: aka.ms/pythonai/resources
๐ @codeprogrammer
Now you can calmly rewatch the recordings, download the slides, and try the code from each session โ from LLM and RAG to AI agents and MCP.
All resources are here: aka.ms/pythonai/resources
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โค10๐3๐2๐ฅ1
๐ 23 Years of SPOTO โ Claim Your Free IT Certs Prep Kit!
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๐ฅWhether you're preparing for #Python, #AI, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #Excel, #comptia, #ITIL, #cloud or any other in-demand certification โ SPOTO has got you covered!
โ Free Resources :
ใปFree Python, Excel, Cyber Security, Cisco, SQL, ITIL, PMP, AWS courses: https://bit.ly/4lk4m3c
ใปIT Certs E-book: https://bit.ly/4bdZOqt
ใปIT Exams Skill Test: https://bit.ly/4sDvi0b
ใปFree AI material and support tools: https://bit.ly/46TpsQ8
ใปFree Cloud Study Guide: https://bit.ly/4lk3dIS
๐ Become Part of Our IT Learning Circle! resources and support:
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wa.link/rozuuw
Do you want to understand the methods used to train LLMs?
The training of large language models (LLMs) is based on various approaches that help models understand and generate text.
Each method shapes the learning process in its own way - from predicting the next word to classifying entire sentences or labeling entities.
Here are 4 common methods of training LLMs in simple language ๐
1. Causal Language Modeling
Predicts the next word in a sequence based on the previous ones. Helps the model master the natural flow of speech and the structure of sentences.
Analogy: how to finish a sentence for another person by guessing the next word.
2. Masked Language Modeling
Learns by guessing the missing words in a sentence based on the surrounding context. Improves the overall understanding of language.
Analogy: how to solve tasks with missing words.
3. Text Classification Modeling
Determines the general class of a sentence (for example, tone or topic) by comparing predictions with actual labels.
Analogy: how to sort letters into folders "Work", "Personal", or "Promotions".
4. Token Classification Modeling
Assigns labels to each word or subword - for example, highlights names, places, or dates in the text.
Analogy: how to highlight words with different colors - names in blue, places in green, dates in yellow.
These methods form the basis of modern LLMs, and each of them plays a role in making AI smarter and more useful.
https://t.me/CodeProgrammer
The training of large language models (LLMs) is based on various approaches that help models understand and generate text.
Each method shapes the learning process in its own way - from predicting the next word to classifying entire sentences or labeling entities.
Here are 4 common methods of training LLMs in simple language ๐
1. Causal Language Modeling
Predicts the next word in a sequence based on the previous ones. Helps the model master the natural flow of speech and the structure of sentences.
Analogy: how to finish a sentence for another person by guessing the next word.
2. Masked Language Modeling
Learns by guessing the missing words in a sentence based on the surrounding context. Improves the overall understanding of language.
Analogy: how to solve tasks with missing words.
3. Text Classification Modeling
Determines the general class of a sentence (for example, tone or topic) by comparing predictions with actual labels.
Analogy: how to sort letters into folders "Work", "Personal", or "Promotions".
4. Token Classification Modeling
Assigns labels to each word or subword - for example, highlights names, places, or dates in the text.
Analogy: how to highlight words with different colors - names in blue, places in green, dates in yellow.
These methods form the basis of modern LLMs, and each of them plays a role in making AI smarter and more useful.
https://t.me/CodeProgrammer
1โค3๐2
Forwarded from Udemy Coupons
Python Data Analysis Bootcamp - Pandas, Seaborn and Plotly
Complete, in-depth and pratical understanding of modern data analysis techniques....
๐ท Category: it-and-software
๐ Language: English (US)
๐ฅ Students: 17,221 students
โญ๏ธ Rating: 4.5/5.0 (113 reviews)
๐โโ๏ธ Enrollments Left: 48
โณ Expires In: 0D:10H:10M
๐ฐ Price:$9.59 => FREE
๐ Coupon: F888C355AA9260F585D7
โ ๏ธ Please note: A verification layer has been added to prevent bad actors and bots from claiming the courses, so it is important for genuine users to enroll manually to not lose this free opportunity.
๐ By: https://t.me/DataScienceC
Complete, in-depth and pratical understanding of modern data analysis techniques....
๐ท Category: it-and-software
๐ Language: English (US)
๐ฅ Students: 17,221 students
โญ๏ธ Rating: 4.5/5.0 (113 reviews)
๐โโ๏ธ Enrollments Left: 48
โณ Expires In: 0D:10H:10M
๐ฐ Price:
๐ Coupon: F888C355AA9260F585D7
โ ๏ธ Please note: A verification layer has been added to prevent bad actors and bots from claiming the courses, so it is important for genuine users to enroll manually to not lose this free opportunity.
๐ By: https://t.me/DataScienceC