Once you start visualizing your SQL schemas like this, there's no going back.
sqltoerdiagram.com
https://t.me/DataAnalyticsX
sqltoerdiagram.com
https://t.me/DataAnalyticsX
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Forwarded from Machine Learning with Python
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
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Forwarded from Machine Learning with Python
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 is not an advertisement: I personally used it and decided to share it with you.
https://deep-ml.com
https://t.me/CodeProgrammer
Forwarded from Machine Learning
pandas_vs_polars_cheatsheet.png
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Have you seen the Stanford lecture notes on GPU architecture and CUDA programming?
Excellent course!
https://gfxcourses.stanford.edu/cs149/fall25/lecture/gpuarch/
https://t.me/DataAnalyticsX✅
Excellent course!
https://gfxcourses.stanford.edu/cs149/fall25/lecture/gpuarch/
https://t.me/DataAnalyticsX
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This section contains reference materials for Python, presented concisely, structured, and with ready-to-use code examples. It includes a general cheat sheet for the language, as well as separate materials on specific libraries and development areas. Multithreading, multiprocessing, asyncio, GIL, and other topics are also covered in detail.
📌 Here's the link: kb.txtly.ru
👉Russian lang
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Have you seen the interactive explanation of the Transformer?
It's a really cool visualization of how the architecture works.
https://poloclub.github.io/transformer-explainer/
It's a really cool visualization of how the architecture works.
https://poloclub.github.io/transformer-explainer/
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Forwarded from Machine Learning with Python
"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🤩
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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Forwarded from Machine Learning with Python
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
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