👩🏻💻 Guys , GenAI Community has created a free Python training course where you will learn both Python and its application in generative artificial intelligence!
Here are some suitable hashtags for the description in English, in a single line:
#Python #GenAI #FreeCourse #ArtificialIntelligence #DataScience #Programming #LearnToCode #AI #MachineLearning #Coding #Tech #Education #OnlineLearning #FreeTraining #GenerativeAI
https://t.iss.one/CodeProgrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
👍11❤7
@Codeprogrammer Cheat Sheet Numpy.pdf
213.7 KB
This checklist covers the essentials of NumPy in one place, helping you:
- Create and initialize arrays
- Perform element-wise computations
- Stack and split arrays
- Apply linear algebra functions
- Efficiently index, slice, and manipulate arrays
…and much more!
Feel free to share if you found this useful, and let me know in the comments if I missed anything!
⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
- Create and initialize arrays
- Perform element-wise computations
- Stack and split arrays
- Apply linear algebra functions
- Efficiently index, slice, and manipulate arrays
…and much more!
Feel free to share if you found this useful, and let me know in the comments if I missed anything!
#NumPy #Python #DataScience #MachineLearning #Automation #DeepLearning #Programming #Tech #DataAnalysis #SoftwareDevelopment #Coding #TechTips #PythonForDataScience
Please open Telegram to view this post
VIEW IN TELEGRAM
❤9👍8
Forwarded from Python Courses & Resources
Media is too big
VIEW IN TELEGRAM
14 minutes with an Anthropic engineer will teach you more about building agents 🤖 than most devs figure out in months of trial and error 🛠.
Same guy who wrote “Building Effective Agents”, the post every AI builder has bookmarked 📑.
No fluff. No 47-tool frameworks. Just the patterns that actually work in production 🚀:
→ When to use workflows vs. agents (most people get this wrong) ❌
→ Why simple > clever, every single time ✅
→ The orchestrator-worker pattern that scales 📈
→ When NOT to build an agent at all 🛑
If you’re shipping AI products in 2026 and haven’t watched this, you’re doing it on hard mode 🎮.
14 minutes. Bookmark it 📌. Watch it twice 👀.
#AI #Agents #Tech #DevCommunity #FutureTech #ProgrammingConcepts
Same guy who wrote “Building Effective Agents”, the post every AI builder has bookmarked 📑.
No fluff. No 47-tool frameworks. Just the patterns that actually work in production 🚀:
→ When to use workflows vs. agents (most people get this wrong) ❌
→ Why simple > clever, every single time ✅
→ The orchestrator-worker pattern that scales 📈
→ When NOT to build an agent at all 🛑
If you’re shipping AI products in 2026 and haven’t watched this, you’re doing it on hard mode 🎮.
14 minutes. Bookmark it 📌. Watch it twice 👀.
#AI #Agents #Tech #DevCommunity #FutureTech #ProgrammingConcepts
❤10👍3🎉2
reader3 📚✨
When you want to connect an AI like Gemini to help you analyze books or content, copying text from a reader usually becomes a hassle. 😩💻
Especially if you want to discuss a book by chapters. Highlighting text manually and copying it disrupts the flow and feels like a waste of time. ⏳🚫
Yesterday, Andrzej Karpati, a well-known AI expert, released a new project to the public: reader3, which solves this problem very neatly. 🎉🛠️ It's a lightweight EPUB reader that allows you to read a book together with AI. 🤖📖
Its interface is as minimalist as possible: only the necessary reading and navigation functions. 📉🧭 You can also manage your library through folders. 📁✨
The key feature is that it breaks an EPUB into chapters and displays the content one chapter at a time. 🔓📄
This makes it easy to copy the needed part of the book and pass it to a large model for analysis or discussion. 📋🔄 It significantly improves the reading experience when paired with AI. 🚀🧠
And it's very easy to get started - just run two commands via uv. ⚡🛠️ As a result, it's an excellent tool for those who love reading and want to use AI as a companion for text analysis. 📚🤝🤖
📁 Language: #Python 61.0%
⭐️ Stars: 1.5k
➡️ Link to GitHub https://github.com/karpathy/reader3
#AI #Python #Reader3 #Tech #BookLovers #Github
https://t.iss.one/CodeProgrammer✅
When you want to connect an AI like Gemini to help you analyze books or content, copying text from a reader usually becomes a hassle. 😩💻
Especially if you want to discuss a book by chapters. Highlighting text manually and copying it disrupts the flow and feels like a waste of time. ⏳🚫
Yesterday, Andrzej Karpati, a well-known AI expert, released a new project to the public: reader3, which solves this problem very neatly. 🎉🛠️ It's a lightweight EPUB reader that allows you to read a book together with AI. 🤖📖
Its interface is as minimalist as possible: only the necessary reading and navigation functions. 📉🧭 You can also manage your library through folders. 📁✨
The key feature is that it breaks an EPUB into chapters and displays the content one chapter at a time. 🔓📄
This makes it easy to copy the needed part of the book and pass it to a large model for analysis or discussion. 📋🔄 It significantly improves the reading experience when paired with AI. 🚀🧠
And it's very easy to get started - just run two commands via uv. ⚡🛠️ As a result, it's an excellent tool for those who love reading and want to use AI as a companion for text analysis. 📚🤝🤖
📁 Language: #Python 61.0%
⭐️ Stars: 1.5k
➡️ Link to GitHub https://github.com/karpathy/reader3
#AI #Python #Reader3 #Tech #BookLovers #Github
https://t.iss.one/CodeProgrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
❤6👍4🔥2👎1🆒1
Forwarded from Learn Python Coding
Cheat sheet on the basics of Python: 🐍📚
basic syntax and language rules 📝
scalar types — basic data types (int, float, bool, str, NoneType) 🔢
datetime — working with date and time 📅⏰
data structures — Python data structures (list, tuple, dict, set) 🗄
list — mutable lists for storing data collections 📋
tuple — immutable sequences of values 🔒
dict (hash map) — storing data in a key-value format 🗝
set — unique elements without order 🔘
slicing — obtaining parts of sequences through indices and step ✂️
module/library — connecting modules and libraries 🔌
help functions — using help() and dir() to explore the Python API 🛠
#Python #Coding #DataScience #Programming #Tech #DevCommunity
basic syntax and language rules 📝
scalar types — basic data types (int, float, bool, str, NoneType) 🔢
datetime — working with date and time 📅⏰
data structures — Python data structures (list, tuple, dict, set) 🗄
list — mutable lists for storing data collections 📋
tuple — immutable sequences of values 🔒
dict (hash map) — storing data in a key-value format 🗝
set — unique elements without order 🔘
slicing — obtaining parts of sequences through indices and step ✂️
module/library — connecting modules and libraries 🔌
help functions — using help() and dir() to explore the Python API 🛠
#Python #Coding #DataScience #Programming #Tech #DevCommunity
❤3👏2👎1
Forwarded from Machine Learning
🚀 Master Binary Classification with Neural Networks! 🧠✨
Ever wondered how to build a neural network from scratch in Python using NumPy? 🐍📊
Binary classification is at the heart of many machine learning applications. 🎯🤖
Our super-detailed guide walks you through the entire process step by step. 📝📚
💡 Dive in and start building your own neural network today! 🏗🔥
https://tinztwinshub.com/data-science/a-beginners-guide-to-developing-an-artificial-neural-network-from-zero/
#MachineLearning #NeuralNetworks #Python #DataScience #AI #Tech
Ever wondered how to build a neural network from scratch in Python using NumPy? 🐍📊
Binary classification is at the heart of many machine learning applications. 🎯🤖
Our super-detailed guide walks you through the entire process step by step. 📝📚
💡 Dive in and start building your own neural network today! 🏗🔥
https://tinztwinshub.com/data-science/a-beginners-guide-to-developing-an-artificial-neural-network-from-zero/
#MachineLearning #NeuralNetworks #Python #DataScience #AI #Tech
❤8👎1
Forwarded from Machine Learning
🔥 Awesome open-source project to learn more about Transformer Models! 🤖✨
We found this interactive website that shows you visually how transformer models work. 🌐📊
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
#TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech
We found this interactive website that shows you visually how transformer models work. 🌐📊
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
#TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech
❤7👎1👏1
Forwarded from Machine Learning
🔖 A huge open-source course on AI Engineering from scratch
In the repository, we've collected:
— 435 lessons;
— 320+ hours of content;
— Python, TypeScript, and Rust;
— AI agents, MCP servers, prompts, and AI skills.
Moreover, almost every lesson includes practical tasks, so this isn't just theory, but a full-fledged roadmap for AI Engineering. 🚀
⛓️ Link to the repository
https://github.com/rohitg00/ai-engineering-from-scratch
#AI #MachineLearning #Python #Rust #OpenSource #Tech
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
In the repository, we've collected:
— 435 lessons;
— 320+ hours of content;
— Python, TypeScript, and Rust;
— AI agents, MCP servers, prompts, and AI skills.
Moreover, almost every lesson includes practical tasks, so this isn't just theory, but a full-fledged roadmap for AI Engineering. 🚀
⛓️ Link to the repository
https://github.com/rohitg00/ai-engineering-from-scratch
#AI #MachineLearning #Python #Rust #OpenSource #Tech
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
❤9👎1
Transformer implementations for vision, audio, and AI agents 🤖👁️🎵
Repo: https://github.com/Nicolepcx/transformers-the-definitive-guide
#AI #MachineLearning #Vision #Audio #Agents #Tech
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Repo: https://github.com/Nicolepcx/transformers-the-definitive-guide
#AI #MachineLearning #Vision #Audio #Agents #Tech
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
❤4👍3
Interactive Explainer 🧠✨
The Anatomy of an LLM 🔍
A visual walk through the machinery inside a large language model: from raw text, to tokens, to vectors, to attention, to the next token. ⚙️🧬
🔗 Link: https://www.royvanrijn.com/anatomy-of-an-llm/
#LLM #AI #Tech #NeuralNetworks #MachineLearning #DeepLearning
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
The Anatomy of an LLM 🔍
A visual walk through the machinery inside a large language model: from raw text, to tokens, to vectors, to attention, to the next token. ⚙️🧬
🔗 Link: https://www.royvanrijn.com/anatomy-of-an-llm/
#LLM #AI #Tech #NeuralNetworks #MachineLearning #DeepLearning
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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.
❤10
Forwarded from Data Analytics
Transformers & LLMs Cheatsheet.pdf
1.4 MB
The only LLM cheat sheet you'll ever need 🚀
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)
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
#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)
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
#LLM #AI #MachineLearning #DeepLearning #PromptEngineering #Tech
❤6👏1
This media is not supported in your browser
VIEW IN TELEGRAM
✍️ Pyneng — a large base for Python and network automation!
Detailed documentation and educational materials. The site contains lessons on Python syntax, working with files, functions, OOP, as well as separate sections on network technologies. The materials are presented with a large number of examples and practical tasks.
📌 I'll leave a link: https://pyneng.readthedocs.io/en/latest/
#Python #NetworkAutomation #DevOps #Coding #Learning #Tech
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
Detailed documentation and educational materials. The site contains lessons on Python syntax, working with files, functions, OOP, as well as separate sections on network technologies. The materials are presented with a large number of examples and practical tasks.
📌 I'll leave a link: https://pyneng.readthedocs.io/en/latest/
#Python #NetworkAutomation #DevOps #Coding #Learning #Tech
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
❤5🎉1
Forwarded from Data Analytics
The ultimate guide to fine tuning.pdf
15.2 MB
🔖 The Big Book on Fine-Tuning LLMs
A free 115-page book dedicated to the retraining of large language models. 📚
It's suitable for those who want to understand how to prepare datasets, configure training, and improve the quality of LLMs for their tasks. 🚀
#LLM #FineTuning #AI #MachineLearning #DataScience #Tech
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
A free 115-page book dedicated to the retraining of large language models. 📚
It's suitable for those who want to understand how to prepare datasets, configure training, and improve the quality of LLMs for their tasks. 🚀
#LLM #FineTuning #AI #MachineLearning #DataScience #Tech
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
❤5🎉4
5 Fun Papers That Explain LLMs Clearly 📚✨
Want to understand LLMs better? Start with these five foundational papers that explain how they work. 🤖
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. 🧠 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. 📄 This article is part of a fun series where we learn by exploring core ideas, practical projects, and the research papers behind modern technology. 🔬 In this article, we will go through five papers that explain how LLMs work. So, let's get started. 🚀
More: https://www.kdnuggets.com/5-fun-papers-that-explain-llms-clearly
#LLM #AI #MachineLearning #DeepLearning #DataScience #Tech
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
Want to understand LLMs better? Start with these five foundational papers that explain how they work. 🤖
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. 🧠 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. 📄 This article is part of a fun series where we learn by exploring core ideas, practical projects, and the research papers behind modern technology. 🔬 In this article, we will go through five papers that explain how LLMs work. So, let's get started. 🚀
More: https://www.kdnuggets.com/5-fun-papers-that-explain-llms-clearly
#LLM #AI #MachineLearning #DeepLearning #DataScience #Tech
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
❤4