A Chinese developer has released an open-source replacement for NumPy that performs calculations on GPUs. It's called CuPy 🚀. In many cases, it's enough to replace a single line:
The same code can run on CUDA up to 100 times faster ⚡️.
What it can do:
→ Compatible with existing NumPy and SciPy code 🛠️.
→ No need to rewrite the program or learn new syntax 📝.
→ Supports not only CUDA but also AMD ROCm 💻.
The project is completely open-source 📂:
🔗 https://github.com/cupy/cupy
#Python #GPU #NumPy #CuPy #AI #DeepLearning
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import cupy as cp
The same code can run on CUDA up to 100 times faster ⚡️.
What it can do:
→ Compatible with existing NumPy and SciPy code 🛠️.
→ No need to rewrite the program or learn new syntax 📝.
→ Supports not only CUDA but also AMD ROCm 💻.
The project is completely open-source 📂:
🔗 https://github.com/cupy/cupy
#Python #GPU #NumPy #CuPy #AI #DeepLearning
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Don't learn ML by randomly jumping through tutorials. 🚫📚
DS-ML Bootcamp is a public repository for a Data Science and machine learning course for beginners who want a structured path from zero to practical projects. 🚀📊
It helps transition from installation and concepts to practical ML work, organizing lessons, assignments, code examples, datasets, and solutions around the main machine learning workflow. 🛠️🧠
Key features:
- End-to-end workflow - covers data collection, preprocessing, train/test split, model selection, training, evaluation, and deployment 🔄📈
- Lesson-based structure - starts with tools/setup, Data Science, ML, data fundamentals, and regression 📚🧮
- Practical materials - assignments give learners structured tasks, not just reading notes ✍️✅
- Code + datasets - Python examples and raw CSV datasets included for exercises 🐍📂
- Set up for repetition - the README says you can clone the repository and use Jupyter or VS Code while going through lessons 💻🔁
Free public repository on GitHub. 🆓
https://github.com/goobolabs/ds-ml-bootcamp
#MachineLearning #DataScience #Coding #Python #AI #Learning
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DS-ML Bootcamp is a public repository for a Data Science and machine learning course for beginners who want a structured path from zero to practical projects. 🚀📊
It helps transition from installation and concepts to practical ML work, organizing lessons, assignments, code examples, datasets, and solutions around the main machine learning workflow. 🛠️🧠
Key features:
- End-to-end workflow - covers data collection, preprocessing, train/test split, model selection, training, evaluation, and deployment 🔄📈
- Lesson-based structure - starts with tools/setup, Data Science, ML, data fundamentals, and regression 📚🧮
- Practical materials - assignments give learners structured tasks, not just reading notes ✍️✅
- Code + datasets - Python examples and raw CSV datasets included for exercises 🐍📂
- Set up for repetition - the README says you can clone the repository and use Jupyter or VS Code while going through lessons 💻🔁
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GitHub
GitHub - goobolabs/ds-ml-bootcamp: Data Science and Machine Learning Bootcamp. (Jun - 2026)
Data Science and Machine Learning Bootcamp. (Jun - 2026) - goobolabs/ds-ml-bootcamp
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The math.perm() method
The math.perm() method in Python returns the number of ways to select k elements from n elements, with and without repetition. 🧮
Syntax:
Where:
n: The number of elements from which k elements are selected.
k: The number of elements that are selected.
In the first example, the method returns the number of ways to select 3 elements from 5 elements. The result is 60 ways. 📊
In the second example, the method returns the number of ways to select 5 elements from 10 elements. The result is 252 ways. 🚀
#Python #Math #Coding #Programming #DataScience #Tech
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The math.perm() method in Python returns the number of ways to select k elements from n elements, with and without repetition. 🧮
Syntax:
math.perm(n, k)
Where:
n: The number of elements from which k elements are selected.
k: The number of elements that are selected.
In the first example, the method returns the number of ways to select 3 elements from 5 elements. The result is 60 ways. 📊
In the second example, the method returns the number of ways to select 5 elements from 10 elements. The result is 252 ways. 🚀
#Python #Math #Coding #Programming #DataScience #Tech
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