Machine Learning with Python

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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

Red:Machine Learning with Python India5 082 Educación2 402...

📈 Análisis del canal de Telegram Machine Learning with Python

El canal Machine Learning with Python (@codeprogrammer) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 67 828 suscriptores, ocupando la posición 2 402 en la categoría Educación y el puesto 5 082 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 828 suscriptores.

Según los últimos datos del 03 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 63, y en las últimas 24 horas de 3, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.53%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.86% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 1 715 visualizaciones. En el primer día suele acumular 1 262 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 7.
Intereses temáticos: El contenido se centra en temas clave como insidead, learning, degree, evaluation, algorithm.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 04 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.

67 828

Suscriptores

+324 horas

+757 días

+6330 días

1 715

Visitas de la publicación

~ 1 26224 horas

~ 1 72648 horas

2.53%

Tasa de compromiso

~ 3

Mensajes por día

Ads index

beta

Archivo de publicaciones

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"Dive into Deep Learning" 📘🤖 is an open-source book that forms the mathematical foundation for large language models. 🧠📐 It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. 🧮📉🔄 The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. 🚀🔗🧠 It contains over 1,000 pages 📖 and provides clear explanations, practical examples, and exercises. ✅📝 Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. 🌐🔍🤖 arxiv.org/pdf/2106.11342 🔗 #DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource

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Repost 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

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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.

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Do you know that Python can shift sequences without slicing and creating new lists? 🤔 When you need to cyclically shift data, many use slicing:

data = data[-1:] + data[:-1]

But deque.rotate() does this at the level of the data structure and usually works more efficiently for cyclical operations. 🚀

q.rotate(1)

A negative value rotates the queue in the other direction. ⬅️

q.rotate(-2)

This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms. 🔄

workers.rotate(-1)

🔥 deque.rotate() allows you to implement cyclical data structures without manual index logic and without creating new lists. 💡 👉 Python Ready | #tip #Python #Programming #Deque #CodingTips #Tech #DevCommunity

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Do you know that Python can shift sequences without slicing and creating new lists? When you need to cyclically shift data, many use slicing:

data = data[-1:] + data[:-1]

But `deque.rotate()

 does this at the level of the data structure and usually works more efficiently for cyclical operations.

``

python q.rotate(1)

A negative value rotates the queue in the other direction.

python q.rotate(-2)

This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms.

python workers.rotate(-1) ` 🔥 `deque.rotate()` allows you to implement cyclical data structures without manual index logic and without creating new lists. #Python #deque #rotate #Programming #Coding #DevTips

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Repost from Machine Learning

👣 Rust Interview Deep Dive 🦀🔍 A repository for systematic preparation for Rust interviews at the middle, senior, and staff levels. 💼📚 Inside 100 real questions from interviews in product and infrastructure companies, detailed analyses with code examples and scenarios of tasks that occur in production. 💻🏗️ Not "guess the program's output", but the mechanics on which real services are built. 🛠️🚀 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. 🔒⚡ And it all starts with the basics. Ownership, borrowing, lifetimes. 🧱🔄 Those who want can start from scratch or at the staff level. 🚶‍♂️👨‍💻 https://github.com/Develp10/rustinterviewquiestions 🔗 #Rust #Programming #InterviewPrep #SoftwareEngineering #SystemsProgramming #CareerGrowth

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Repost 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

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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.me/CodeProgrammer ✅

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Repost from Machine Learning with Python

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🚀 Demystifying Activation Functions! 🧠✨ Ever wondered why activation functions are so critical in neural networks? 🤔🤖 They’re the secret sauce that allows models to capture complex, nonlinear relationships! 🔥📈 Do you want to learn how to implement an artificial neural network from scratch in Python using NumPy? 🐍📊 Learn more in super-detailed guide: https://lnkd.in/e4CydTtB 🔗📚 #NeuralNetworks #DeepLearning #ActivationFunctions #Python #NumPy #AI

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