Data Analytics

Found an easy way to learn math for ML: Mathematics for Machine Learning 🎓📚 This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊 It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖 Free public repository on GitHub. 💻✨ https://github.com/dair-ai/Mathematics-for-ML #MachineLearning #Mathematics #DataScience #Learning #GitHub #AI

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Pandas vs Polars vs DuckDB: Which Library Should You Choose? 🤔📊 pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows 📝📈. Polars focus on fast, memory-efficient DataFrame processing ⚡💾, while DuckDB brings a SQL-first approach for querying local files and embedded analytics 🗄️🔍. Each tool fits a different kind of local data workflow 🛠️. In this article, we compare pandas, Polars, and DuckDB across performance, architecture, interoperability, and real-world use cases 🏆🔗. More: https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/ 🔗 #DataScience #Pandas #Polars #DuckDB #Python #Analytics

🔥 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

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⚡️ Machine Learning Roadmap 2026: a large map for entering ML without fairy tales about "neural networks in a month" 🤖 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. 🚀 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. 🧠 The roadmap is divided into 7 tracks: 📊 1. Foundation: Python, mathematics, statistics, tools 🏗️ 2. Classic ML: scikit-learn, tabular data, metrics, validation 📈 3. Deep Learning: PyTorch, CNN, RNN, training loop 🧠 4. LLM and transformers: attention, KV-cache, RAG, LoRA, agents 🤖 5. Generative AI: images, videos, audio, multimodality 🎨 6. MLOps and production: Docker, Kubernetes, CI/CD, monitoring, serving ⚙️ 7. Specialization: CV, NLP, RecSys, RL, Safety 🎯 The roadmap doesn't sell the illusion of "training a model - becoming an ML engineer". 🚫 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. 🛠️ 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. 🛑 In terms of time, it's no fairy tale either: ⏳ 1. 0-3 months: mathematics, classic ML 📚 2. 3-6 months: Deep Learning and PyTorch 🔥 3. 6-12 months: LLM, RAG, fine-tuning, AI agents 🤖 4. 12+ months: MLOps, production, scaling, specialization 🚀 Here, seven large free courses on machine learning, mathematics, and Vue coding are also collected! 🎓 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. 🗺️ https://github.com/justxor/MachineLearningRoadmap 🔗 #MachineLearning #AI #DataScience #LLM #MLOps #Python

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

``

#DataStructure #Efficiency #CyclicalOps #Coding #TechTips #Programming

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 #Coding #Programming #DataStructures #TechTips #DevCommunity

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