Machine Learning

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"Learning Mathematics: Why Memory, Practice, and Technique are More Important than Talent" To master mathematics, it is necessary to memorize a large amount of information, rules, methods of simplification, and problem-solving techniques. There is no other way. Theory is useful, but in moderation. It's like learning a language. If you focus too much on grammar, you will never learn to speak it fluently. https://algebrica.org/learning-mathematics/

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

"How to Train a Neural Network" is a concise summary of the MIT course lectures on deep learning from 2024. It focuses on one of the fundamental questions in neural networks: how a model learns its weights. The summary examines the training process from a mathematical perspective. It covers topics such as forward propagation, loss functions, gradients, backpropagation, and gradient-based optimization methods. I believe this is an interesting resource for those who want to go beyond a general, intuitive understanding of neural networks and begin to delve into the mathematics that underlies their training. https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/mit6_7960_f24_lec2.pdf https://t.me/CodeProgrammer 🤩

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If you're just starting to learn machine learning and want to delve deeper into the mathematics required for machine learning and deep learning, I recommend trying this platform. It's something like LeetCode for machine learning. This is not an advertisement: I personally used it and decided to share it with you. https://deep-ml.com https://t.me/CodeProgrammer

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

🤖 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 requests

proxies = {
    "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. 🚀 711Proxy provides real residential IPs across 200+ countries and regions, with SOCKS5 support and sticky sessions — useful for data collection, testing, and other location-based ML workflows. 🎁 1GB free for testing New users can use 711TRIAL to get 1GB of residential proxy traffic. 👉 https://www.711proxy.com After registration, contact 711Proxy support and mention “711TRIAL” to claim the trial. Available to eligible new users.