Explore tweets tagged as #numpy
A cheat sheet of 40 NumPy methods used 95% of the time:
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Python’s power lies in its libraries. From NumPy and Pandas for data analysis, Matplotlib and Plotly for visualization, to TensorFlow, PyTorch, and Scikit-learn for machine learning—these tools turn ideas into real-world solutions. #Python #MachineLearning #DataScience #AI
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The single best way to learn neural networks is to build them from scratch. When I was a grad student, there were no good resources for this, and I struggled for weeks: losing my bearings in object-oriented design, endlessly debugging functions involving NumPy array operations
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How to Get a Job as an AI Developer in 6 Months: 🚀 Month 1 🧠 Learn Python basics 📊 Understand math essentials 📚 Learn how models work at a high level ⚙️ Month 2 📘 Learn NumPy, Pandas, Matplotlib 🤖 Start with Machine Learning 🔍 Build 3 ML mini-projects 🛠️ Month 3 🧠
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Timings of my #AdventOfCode 2025 solutions in #python. I used scipy for day 10 LP and some numpy for day 9. The rest are in plain python 3.13. The task I enjoyed the most was the graph of day 11 - hard at first and so nice when it clicked =).
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Learn NumPy astype to convert array data types efficiently for faster Python data processing and cleaner numerical computations. #NumPy #Python #DataAnalysis #MachineLearning #Coding #Programming
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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（299ページ）が無料公開されている Python3 ライブラリブック https://t.co/niMs14prR8 ・OpenCV ・Pillow ・pygame ・Eel ・PyDub ・NumPy ・matplotlib ・SciPy ・SymPy ・hashlib ・passlib ・Cython ・Numba ・ctypes ・PyInstaller
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Finally after probability statistics, SQL ,Tableau, algebra , calculus, Buisness Case study, python and it's library numpy , Pandas, matplotlib, seaborn It's take me around 40-50 days Today I m going to start Machine Learning from scratch
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That one familiar line in machine learning code: np.random.seed(42) tf.random.set_seed(42) It often looks like 42 is some special number in AI. In practice, it’s simply a random seed used to control randomness across libraries like NumPy and TensorFlow. Setting a seed ensures
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Today in my #MachineLearning course we dive into Generative Adversarial Networks (GANs). After breaking down the core ideas, we rolled up our sleeves and build and train the simplest possible GAN—entirely in @numpy_team! A true nuts-and-bolts approach to explore, learn, and
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I've finished #adventofcode 2025 and this is the first year that I managed to get all solutions under 100ms ⚡ All but two are under 10ms. Not bad for the slowest popular language out there 🐍, although I did have to use numpy a few times.
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All the skills a Data Scientist should check off 📌 A simple checklist to track your progress: ✔️ Python or R ✔️ SQL ✔️ Data Cleaning ✔️ Pandas, NumPy ✔️ Probability and Statistics ✔️ Machine Learning Basics ✔️ Data Visualization (Power BI | Tableau | Matplotlib | Seaborn) ✔️
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