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
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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.

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"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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This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
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5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

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Forwarded from Machine Learning
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I found a great resource for interactive learning about machine learning and AI – VizLearn.

You can experiment with gradient descent, SVM, PCA, the Bayesian method, BPE tokenization, Q/K/V, KV-cache, quantization, and much more. You can change the input data and see how the calculations themselves change.

It's free and doesn't require registration.

https://vizlearn.in

https://t.me/MachineLearning9
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Understanding algorithms changes the way you write code. 🧠 This playlist teaches you exactly that: understanding.

Pavel Mavrin. 61 lectures. Free. 🎓

Here's the difference:

Before understanding algorithms:
- You use hash tables for everything.
- You write the code first, then optimize it.
- You estimate the time complexity randomly.

After:
- You choose the appropriate data structure for the task.
- You think about complexity before writing the code.
- You learn an approach that is suitable for solving the problem.

What you will learn:
→ Time complexity and sorting.
→ Dynamic programming.
→ Advanced trees: segment tree, Fenwick tree, splay tree, link-cut tree.
→ Graph algorithms: traversals, shortest paths, flows.
→ String algorithms: substring search, suffix structures.
→ Fast Fourier Transform, linear programming, cryptography.

https://www.youtube.com/playlist?list=PLrS21S1jm43igE57Ye_edwds_iL7ZOAG4

https://t.me/CodeProgrammer

#Algorithms #ComputerScience #Programming #DataStructures #Learning #Coding

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