Последние посты
Machine Learning pinned «🤖 A Practical Tip for ML Data Collection When building a machine learning project, getting enough useful data is often just as important as the model itself. If you're collecting public web data for a dataset, you may need to access the same source from different…»
20 сент., 11:12
Machine Learning
20 сент., 11:11
🤖 A Practical Tip for ML Data Collection When building a machine learning project, getting enough useful data is often just as important as the model itself. If you're collecting public web data for a dataset, you may need to access the same source from different locations or test how location affects the data returned. A residential proxy can help with this by routing your requests through IPs from different regions. For example, with Python: import requestsproxies = { "http": "http://USER:PASSWORD@HOST:PORT", "https": "http://USER:PASSWORD@HOST:PORT" }response = requests.get( "https://example.com", proxies=proxies )print(response.status_code) Replace USER, PASSWORD, HOST, and PORT with your proxy credentials.711ProxyPremium & Affordable Proxy Services-711ProxyGet residential proxies, unlimited proxies, and static residential proxies from the best provider with 100 million+ IP addresses, get started now!Machine Learning
19 сент., 20:55изменён
🤔 Mathos AI — a neural network for solving and learning mathematics!This AI service helps you break down mathematical problems step-by-step, with explanations for each action. You can enter the problem as text, take a photo, or upload a PDF — Mathos will recognize the problem, suggest a solution, and, if necessary, create a graph. You can request not a ready-made answer, but only a hint, to continue solving the problem yourself.📌 Here's the link: mathos.aihttps://t.me/MachineLearning9Machine Learning
17 сент., 22:11
The correct way to learn is like this:You simply need to solve problems and work on projects.This approach was used in one of the best books on the fundamentals of statistics – and, incidentally, one of the few that I actually read.It's very simple:You read a chapter. You solve all the problems related to that topic.https://www.statlearning.com/
Machine Learning
17 сент., 13:08изменён
🔖 Python Reference for Data Science and Machine LearningPY-DS-ML provides practical resources on 30 popular Python libraries for data analysis and machine learning.You can quickly find the commands, syntax, and examples you need without having to search through extensive documentation.It includes a search function, organization by difficulty level, cheat sheets, and checklists.Link: https://py-ds-ml.ru/#russian #MLMachine Learning
17 сент., 05:36
переслано из @codeprogrammer
Machine Learning
16 сент., 04:01
Get Up to 500MB of Residential Proxy Traffic for Your Python Projects 🐍Building a web scraper with Python? ThorData helps developers collect public web data while handling proxy rotation, geo-targeting, and access restrictions.✅ Residential IPs across 190+ countries ✅ Country, city, and ASN-level targeting ✅ Rotating and sticky sessions ✅ HTTP(S) and SOCKS5 support ✅ Works with Requests, Scrapy, Selenium, and Playwright🎁 Exclusive offer for Machine Learning with Python membersEligible new users can receive up to 500MB of residential proxy traffic for testing.Use channel code: PYTHONSCRAPE👉 Start your test: https://www.thordata.com/?ls=MQiAFqAo&lk=ps-02ThordataThordata - High-Quality Proxy Service for Web Data ScrapingThordata's precision proxy solution was chosen to ensure seamless data collection. Enjoy the best prices and services tailored to your needs.Machine Learning
14 сент., 11:55изменён
🔖 Free book on graph theory"Introduction to Graph Theory" by Darij Grinberg is an advanced resource with theorems, an algebraic approach, and plenty of exercises.You can read it for free with an AI tutor.⛓ Link to the book https://chapterpal.com/ebook/473a1c1b-c584-4430-b986-cd9c57085e67
Machine Learning
14 сент., 05:33
Machine Learning
13 сент., 05:41
🚀Round 2 – 14-Day CCNA & CCNP Study Sprint!Our first 21-Day Sprint was a huge success — we saw amazing check-ins, great discussions, and a community that truly learned together. 🙌Now we're back with a faster, tighter 14-Day Sprint — same energy, same prizes, easier to finish! 💪📅 Sprint Period: Sep 14 – Sep 27 (UTC+8) 📝 How It Works: ① DM admin: "I'M IN + cert name" (e.g., I'M IN CCNA 200-301) ② Check in 12 out of 14 days → win prizes 🎁🏆 Prizes (First come, first served!): 1️⃣ $10 SPOTO Universal Coupon ×2 2️⃣ Cisco SD-Access Training ($150 value) ×1 3️⃣ Cisco SD-WAN Training ($150 value) ×1 4️⃣ CCNA Training Pro Package ($59.99 value) ×10 5️⃣ Free Cisco Learning Pack – unlimited for all finishers 🎁✅ Referral Bonus: Invite a friend → Get FREE EXAM DEMO 💻 DM admin to register: https://wa.me/8619559123054
Machine Learning
12 сент., 08:38
переслано из @courses27
🎓 Deep Learning for Images with PyTorch: CNNs to GANs#Data_Science #DataCamp🏫 Platform: DataCamp 🆓 100% FREE━━━━━━━━━━━━━━━━━━━━ 📝 Course Details: This advanced computer vision course delivers a hands-on exploration of PyTorch across all major vision tasks. From Convolutional Neural Networks (CNNs) for image classification to advanced segmentation masks and Generative Adversarial Networks (GANs), it prepares practitioners for complex computer vision engineering tasks. Who It's For Advanced PyTorch practitioners, computer vision engineers, and machine learning research engineers seeking deep technical expertise in image processing and synthesis. Key Takeaways • CNNs & Object Detection: Train CNNs for binary and multi-class classification, leverage pre-trained models, and evaluate object detection using bounding boxes. …📢 Channel: https://t.me/Courses27
Machine Learning
12 сент., 05:38изменён
This repository contains Jupyter notebooks for the O'Reilly book "Transformers: The Definitive Guide."It includes code for computer vision tasks, time series analysis, audio processing, and reinforcement learning.https://github.com/Nicolepcx/transformers-the-definitive-guidehttps://t.me/MachineLearning9 🤩
Machine Learning
11 сент., 16:08
🧲 Your agent writes the tool. You keep the terminal closed.You know the shape of the script before you open the editor. The hour goes to argparse, a retry wrapper, a rate limiter you have written eleven times already.Create your own AI agent inside Telegram in about a minute, and create small tools with it right in the chat.▫️ describe a tool in a sentence and it writes, runs and returns the working script ▫️ ships a mini-app inside Telegram — a form, a converter, a dashboard, no deploy and no hosting ▫️ drop in a traceback or a repo link and get the fix, not a lecture ▫️ swap the model per task with one command, so cheap work runs cheap ▫️ remembers your stack, your conventions and your project for months ▫️ voice in, answer back — describe the task on the way home, read the result when you are backSetup takes a minute: open the link, start theMachine Learning pinned a photo
11 сент., 06:06
Machine Learning
11 сент., 04:24
🌈 2026 Job-Seeker Toolkit – Free Interview & IT Cert Resources🔥The 2026 hiring market is shifting fast. We've put together a 100% free resource bundle covering #Cisco, #AWS, #PMP, #AI, #Python, #Excel, and #Cybersecurity — including: ✅Q&A banks & mock exams ✅Behavioral interview guides ✅Technical deep-dives for coding & infrastructure roles ✅Real-world project scenariosPerfect for Software Developer Jobs, IT Internships, and Python Projects practice.🎯 Interview Question Bank → https://bit.ly/4A6m0hM 🪜 Online Free Course For Python & Excel→https://bit.ly/46bUzWm 📘 Free Cert E‑Book → https://bit.ly/4xXkAVx ☁️ Free AI Materials → https://bit.ly/4xMBLJd 📊 Cloud Study Guide → https://bit.ly/4cAQ8rN 🧠 Free Mock Exam → https://bit.ly/4xcp3CxTag a friend who's job-hunting or grinding Python projects — let's ace it together! 💪 🧠 Join Study Community: https://chat.whatsapp.com/Dcp
Machine Learning
10 сент., 10:06изменён
🔖 Learning Mathematics — How to Stop Being Afraid of MathThe author explains that mathematical thinking is not an innate talent, but a skill that develops gradually through practice, time, and systematic work.Useful reading for those who are building their math foundation for Data Science and Machine Learning.⛓Link to the book
Machine Learning
8 сент., 07:02изменён
📚 This is probably one of the best technical books on how large language models are trained at scale:> GPU memory and profiling > Breaking down computations into blocks, kernel fusion, and FlashAttention > Data parallelism, tensor parallelism, pipeline parallelism, and context parallelismI've already read the free online version, but I still had to buy a physical copy for my library. 📖You can also read it for free on Hugging Face:https://huggingface.co/spaces/nanotron/ultrascale-playbook#LLM #AI #MachineLearning #TechBooks #DataScience #Coding✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Machine Learning
8 сент., 06:31
🧲 Forty papers open, and you still cannot say which model to take. Benchmarks disagree, model cards hide the licence, and the thread that actually explains the tradeoff is on someone's timeline from March. Create your own AI agent inside Telegram in about1,990Открыть в TelegramMachine Learning
5 сент., 11:51
🔖Computer Science Fundamentals from MITWe found the textbook Mathematics for Computer Science – covering the mathematics that underlies algorithms and computer science.Logic, graphs, combinatorics, probability, induction, recurrence relations, and discrete structures – all in one place.⛓️ Link to the textbook https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-spring-2015/mit6_042js15_textbook.pdfMachine Learning
5 сент., 08:27изменён
