๐๐ธ 500$ FOR THE FIRST 500 WHO JOIN THE CHANNEL! ๐๐ธ
Join our channel today for free! Tomorrow it will cost 500$!
https://t.me/+-WZeIeP8YI8wM2E6
You can join at this link! ๐๐
https://t.me/+-WZeIeP8YI8wM2E6
Join our channel today for free! Tomorrow it will cost 500$!
https://t.me/+-WZeIeP8YI8wM2E6
You can join at this link! ๐๐
https://t.me/+-WZeIeP8YI8wM2E6
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๐ Demystifying Activation Functions! ๐ง โจ
Ever wondered why activation functions are so critical in neural networks? ๐ค๐ค
Theyโre the secret sauce that allows models to capture complex, nonlinear relationships! ๐ฅ๐
Do you want to learn how to implement an artificial neural network from scratch in Python using NumPy? ๐๐
Learn more in super-detailed guide: https://lnkd.in/e4CydTtB ๐๐
#NeuralNetworks #DeepLearning #ActivationFunctions #Python #NumPy #AI
Ever wondered why activation functions are so critical in neural networks? ๐ค๐ค
Theyโre the secret sauce that allows models to capture complex, nonlinear relationships! ๐ฅ๐
Do you want to learn how to implement an artificial neural network from scratch in Python using NumPy? ๐๐
Learn more in super-detailed guide: https://lnkd.in/e4CydTtB ๐๐
#NeuralNetworks #DeepLearning #ActivationFunctions #Python #NumPy #AI
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reader3 ๐โจ
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. ๐ฉ๐ป
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. โณ๐ซ
Yesterday, Andrzej Karpati, a well-known AI expert, released a new project to the public: reader3, which solves this problem very neatly. ๐๐ ๏ธ It's a lightweight EPUB reader that allows you to read a book together with AI. ๐ค๐
Its interface is as minimalist as possible: only the necessary reading and navigation functions. ๐๐งญ You can also manage your library through folders. ๐โจ
The key feature is that it breaks an EPUB into chapters and displays the content one chapter at a time. ๐๐
This makes it easy to copy the needed part of the book and pass it to a large model for analysis or discussion. ๐๐ It significantly improves the reading experience when paired with AI. ๐๐ง
And it's very easy to get started - just run two commands via uv. โก๐ ๏ธ As a result, it's an excellent tool for those who love reading and want to use AI as a companion for text analysis. ๐๐ค๐ค
๐ Language: #Python 61.0%
โญ๏ธ Stars: 1.5k
โก๏ธ Link to GitHub https://github.com/karpathy/reader3
#AI #Python #Reader3 #Tech #BookLovers #Github
https://t.me/CodeProgrammerโ
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. ๐ฉ๐ป
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. โณ๐ซ
Yesterday, Andrzej Karpati, a well-known AI expert, released a new project to the public: reader3, which solves this problem very neatly. ๐๐ ๏ธ It's a lightweight EPUB reader that allows you to read a book together with AI. ๐ค๐
Its interface is as minimalist as possible: only the necessary reading and navigation functions. ๐๐งญ You can also manage your library through folders. ๐โจ
The key feature is that it breaks an EPUB into chapters and displays the content one chapter at a time. ๐๐
This makes it easy to copy the needed part of the book and pass it to a large model for analysis or discussion. ๐๐ It significantly improves the reading experience when paired with AI. ๐๐ง
And it's very easy to get started - just run two commands via uv. โก๐ ๏ธ As a result, it's an excellent tool for those who love reading and want to use AI as a companion for text analysis. ๐๐ค๐ค
๐ Language: #Python 61.0%
โญ๏ธ Stars: 1.5k
โก๏ธ Link to GitHub https://github.com/karpathy/reader3
#AI #Python #Reader3 #Tech #BookLovers #Github
https://t.me/CodeProgrammer
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Forwarded from Learn Python Coding
Cheat sheet on the basics of Python: ๐๐
basic syntax and language rules ๐
scalar types โ basic data types (int, float, bool, str, NoneType) ๐ข
datetime โ working with date and time ๐ โฐ
data structures โ Python data structures (list, tuple, dict, set) ๐
list โ mutable lists for storing data collections ๐
tuple โ immutable sequences of values ๐
dict (hash map) โ storing data in a key-value format ๐
set โ unique elements without order ๐
slicing โ obtaining parts of sequences through indices and step โ๏ธ
module/library โ connecting modules and libraries ๐
help functions โ using help() and dir() to explore the Python API ๐
#Python #Coding #DataScience #Programming #Tech #DevCommunity
basic syntax and language rules ๐
scalar types โ basic data types (int, float, bool, str, NoneType) ๐ข
datetime โ working with date and time ๐ โฐ
data structures โ Python data structures (list, tuple, dict, set) ๐
list โ mutable lists for storing data collections ๐
tuple โ immutable sequences of values ๐
dict (hash map) โ storing data in a key-value format ๐
set โ unique elements without order ๐
slicing โ obtaining parts of sequences through indices and step โ๏ธ
module/library โ connecting modules and libraries ๐
help functions โ using help() and dir() to explore the Python API ๐
#Python #Coding #DataScience #Programming #Tech #DevCommunity
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Forwarded from Machine Learning
๐ฃ Rust Interview Deep Dive ๐ฆ๐
A repository for systematic preparation for Rust interviews at the middle, senior, and staff levels. ๐ผ๐
Inside 100 real questions from interviews in product and infrastructure companies, detailed analyses with code examples and scenarios of tasks that occur in production. ๐ป๐๏ธ Not "guess the program's output", but the mechanics on which real services are built. ๐ ๏ธ๐
Here are lock-free structures, self-referential types in async, FFI with tensor libraries, correct Send on guards via await, memory ordering under loom, soundness of custom collections. ๐โก And it all starts with the basics. Ownership, borrowing, lifetimes. ๐งฑ๐ Those who want can start from scratch or at the staff level. ๐ถโโ๏ธ๐จโ๐ป
https://github.com/Develp10/rustinterviewquiestions ๐
#Rust #Programming #InterviewPrep #SoftwareEngineering #SystemsProgramming #CareerGrowth
A repository for systematic preparation for Rust interviews at the middle, senior, and staff levels. ๐ผ๐
Inside 100 real questions from interviews in product and infrastructure companies, detailed analyses with code examples and scenarios of tasks that occur in production. ๐ป๐๏ธ Not "guess the program's output", but the mechanics on which real services are built. ๐ ๏ธ๐
Here are lock-free structures, self-referential types in async, FFI with tensor libraries, correct Send on guards via await, memory ordering under loom, soundness of custom collections. ๐โก And it all starts with the basics. Ownership, borrowing, lifetimes. ๐งฑ๐ Those who want can start from scratch or at the staff level. ๐ถโโ๏ธ๐จโ๐ป
https://github.com/Develp10/rustinterviewquiestions ๐
#Rust #Programming #InterviewPrep #SoftwareEngineering #SystemsProgramming #CareerGrowth
GitHub
GitHub - Develp10/rustinterviewquiestions: Rust ะฒะพะฟะพััั ั ัะพะฑะตัะตะดะพะฒะฐะฝะธะน
Rust ะฒะพะฟะพััั ั ัะพะฑะตัะตะดะพะฒะฐะฝะธะน . Contribute to Develp10/rustinterviewquiestions development by creating an account on GitHub.
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AI is moving fast. Accountability is not.
That is why we built the open source core of Forkit Dev.
Forkit Dev introduces Model Passports and Agent Passports so AI systems can be tracked, verified, and understood across their lifecycle.
Open source repo:
https://github.com/arpitasarker01/Forkit_Dev
If you care about trustworthy AI, open source infrastructure, model lineage, or compliance ready deployment, check it out and share your thoughts.
That is why we built the open source core of Forkit Dev.
Forkit Dev introduces Model Passports and Agent Passports so AI systems can be tracked, verified, and understood across their lifecycle.
Open source repo:
https://github.com/arpitasarker01/Forkit_Dev
If you care about trustworthy AI, open source infrastructure, model lineage, or compliance ready deployment, check it out and share your thoughts.
GitHub
GitHub - Forkit-Dev-Core/Forkit_Dev: Forkit Core is an open source passport layer for AI models and agents with GitHub CI validationโฆ
Forkit Core is an open source passport layer for AI models and agents with GitHub CI validation, local verification, and Hugging Face-compatible export. - Forkit-Dev-Core/Forkit_Dev
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Machine Learning with Python pinned ยซAI is moving fast. Accountability is not. That is why we built the open source core of Forkit Dev. Forkit Dev introduces Model Passports and Agent Passports so AI systems can be tracked, verified, and understood across their lifecycle. Open source repo:โฆยป
Forwarded from Machine Learning
๐ Master Binary Classification with Neural Networks! ๐ง โจ
Ever wondered how to build a neural network from scratch in Python using NumPy? ๐๐
Binary classification is at the heart of many machine learning applications. ๐ฏ๐ค
Our super-detailed guide walks you through the entire process step by step. ๐๐
๐ก Dive in and start building your own neural network today! ๐๐ฅ
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? ๐๐
Binary classification is at the heart of many machine learning applications. ๐ฏ๐ค
Our super-detailed guide walks you through the entire process step by step. ๐๐
๐ก Dive in and start building your own neural network today! ๐๐ฅ
https://tinztwinshub.com/data-science/a-beginners-guide-to-developing-an-artificial-neural-network-from-zero/
#MachineLearning #NeuralNetworks #Python #DataScience #AI #Tech
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"Dive into Deep Learning" ๐๐ค is an open-source book that forms the mathematical foundation for large language models. ๐ง ๐
It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. ๐งฎ๐๐
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. ๐๐๐ง
It contains over 1,000 pages ๐ and provides clear explanations, practical examples, and exercises. โ ๐ Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. ๐๐๐ค
arxiv.org/pdf/2106.11342 ๐
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. ๐งฎ๐๐
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. ๐๐๐ง
It contains over 1,000 pages ๐ and provides clear explanations, practical examples, and exercises. โ ๐ Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. ๐๐๐ค
arxiv.org/pdf/2106.11342 ๐
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
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Forwarded from Machine Learning
๐ฅ Awesome open-source project to learn more about Transformer Models! ๐คโจ
We found this interactive website that shows you visually how transformer models work. ๐๐
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. ๐๐
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
#TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech
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Forwarded from Data Analytics
Pandas vs Polars vs DuckDB: Which Library Should You Choose? ๐ค๐
pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows ๐๐. Polars focus on fast, memory-efficient DataFrame processing โก๐พ, while DuckDB brings a SQL-first approach for querying local files and embedded analytics ๐๏ธ๐.
Each tool fits a different kind of local data workflow ๐ ๏ธ. In this article, we compare pandas, Polars, and DuckDB across performance, architecture, interoperability, and real-world use cases ๐๐.
More: https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/ ๐
#DataScience #Pandas #Polars #DuckDB #Python #Analytics
pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows ๐๐. Polars focus on fast, memory-efficient DataFrame processing โก๐พ, while DuckDB brings a SQL-first approach for querying local files and embedded analytics ๐๏ธ๐.
Each tool fits a different kind of local data workflow ๐ ๏ธ. In this article, we compare pandas, Polars, and DuckDB across performance, architecture, interoperability, and real-world use cases ๐๐.
More: https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/ ๐
#DataScience #Pandas #Polars #DuckDB #Python #Analytics
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