Explore tweets tagged as #numpy
Bibliotecas Python Essenciais para Ciência de Dados. Bibliotecas Principais.1. NumPy.2. Pandas. Visualização de Dados. 3. Matplotlib.4. Seaborn.5. Plotly. ML. 6. Scikit-learn.7. XGBoost.8. LightGBM.9. CatBoost.10. PyCaret .11. Auto-sklearn.12. H2O.13. TPOT .14. Optuna.15. FLAML
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ML newbie’s looking at LLM architecture after learning NumPy
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The math behind the Python Quant Scientist Stack:. • SciPy: $0.• Zipline: $0.• Python: $0.• NumPy: $0.• PyFolio: $0.• pandas : $0.• OpenBB: $0.• Empyrical: $0.• AlphaLens: $0.• Statsmodels: $0.• RiskFolio-Lib: $0. You can start algorithmic trading for free. Want help?
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Pythonのライブラリを大量に解説した長編テキスト（276ページ）が無料公開されている. Python3 ライブラリブック. ・OpenCV.・Pillow.・pygame.・Eel.・PyDub.・NumPy.・matplotlib.・SciPy.・SymPy.・hashlib.・passlib.・Cython.・Numba.・ctypes.・PyInstaller
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some backstory: when i first started to use numpy (a bit after my 12th grade) i was surprised to see how fast it was and most importantly, how it handled n-dimensional arrays not just two. i remember writing a simple matmul in C and thinking what would it take to go from two to
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🚀Day-4 :. Of my Python Learning Journey .--> Numpy fundamental library for scientific computing . 🌐.👉 Supports multi-dimensional arrays.👉 Super fast mathematical operations.👉 Great for data analysis, machine learning, and scientific computing
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If you want to improve your Data Science and Python skills, this course is for you. You'll use popular Python libraries like Pandas, scikit-learn, and NumPy to extract and clean data, then analyze it. You'll also learn about grouping & aggregation functions, merging datasets,
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30 days update ✅. • finished basics of python, pandas & numpy. • trained my first linear regression model. • started learning scikit-learn. • solved 1000+ math Qs (linear algebra, calculus).• started probability & stats. • crossed 250. thank y’all ❤️
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Python is slow. C is fast. But Python library X (e.g. numpy) is fast. Yes, I said C is fast.
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Throwback to 2019 when I built my first Python project - A paint application using opencv and numpy
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Python Data Science Handbook ile IPython, NumPy, Pandas, Matplotlib ve Scikit-Learn ile veri bilimine sağlam bir giriş yap!.Ücretsiz, açık kaynak ve Jupyter not defterleriyle uygulamalı. 🔗
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6. Machine Learning Specialization. Learners will be able to:.➜ Build ML models using Python libraries like NumPy and scikit-learn. ➜ Train neural networks with TensorFlow for multi-class classification..
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🚀 Day 22 Progress.Learnt Pandas & NumPy in my Data Science course 📊.✔️ Worked on DataFrames 💻.⚡ Less time these days, but still trying my best ⏳.Streak alive 💪.#100DaysOfCode #Python #DataScience #Pandas #NumPy
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Punched a web3 guy because he said ML is all about importing NumPy as np
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For MLE roles, your resume needs to speak both engineering and ML. Recruiters (and ATS) skim for specific tools, so make sure you include the right ones. At a minimum, your resume should mention Python, SQL, NumPy, Pandas, scikit-learn, and either PyTorch or TensorFlow. If
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🎫NumPy Series. np.random.random((i, j)) generates random floating point numbers between 0 and 1. > When to use it?. 1. Random sampling → pick probabilities. 2. Initializing weights in ML models. 3. Data augmentation → adding random noise. #AI #buildinpublic #datascience #Numpy
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