Search Results for "numpy python 3.12"
Sort By:
Showing 12 open source projects for "numpy python 3.12"
View related business solutions-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
Veeam Data Platform v13.1 - Get Your Free TrialTry Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
-
1
Python Tutorial
Xiaobai Python Tutorial
Python Tutorial is a Chinese beginner-friendly Python tutorial repository focused on practical self-study. It is written for learners who want to build programming fundamentals step by step instead of collecting scattered resources without a path. The course is based on Python 3.10+ and marks newer language features from Python 3.11, 3.12, and 3.13 where relevant. -
2
Complete-Python-3-Bootcamp
Course Files for Complete Python 3 Bootcamp Course on Udemy
...In addition, it includes applied exercises in areas such as web scraping, working with APIs, and using Python libraries like NumPy, pandas, Matplotlib, and Seaborn for data analysis and visualization. Learners can progress from beginner-friendly basics to more advanced programming skills while reinforcing their knowledge with practice problems and projects. Because it mirrors the course content, this repository is widely used by students taking the Udemy course. -
3
Little Book of Linear Algebra
A concise, beginner-friendly introduction to the core ideas of linear
...The material is organized into chapters covering vectors, matrices, linear systems, vector spaces, eigenvalues/eigenvectors, and other central topics, each with worked examples and explanations. There is also a companion “LAB” section for hands-on exploration (e.g. using Python/NumPy) to help cement the connections between algebraic formulas and computational behavior. The exposition aims to sit between a pop-math summary and a heavy textbook: definitions and key theorems are stated cleanly, while proofs are sometimes omitted or sketched to keep the flow digestible. Because of its brevity and clarity, it's especially useful as a first pass for learners who want a solid map of the subject before diving into full textbooks. -
4
KherveBook
Computational notebook mixing Python, Markdown, LaTeX, JS and Sheet
KherveBook is a Jupyter-inspired computational notebook built as a native PyQt5 desktop application. A single document mixes five kinds of cell: runnable Python code, formatted Markdown, typeset LaTeX, JavaScript/HTML, and live spreadsheets. Python runs in an in-process kernel with NumPy, SciPy, pandas and Matplotlib preloaded. A side AI chat that knows the API of KherveBook that can write your Python program for you. Spreadsheet cells evaluate A1-style formulas in Python; LaTeX cells compile full documents with tectonic; JavaScript/HTML cells render in an embedded web view (D3, Plotly, canvas); and images or PDF pages import as cells. ... -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
-
5
Prime number ( primenumbers )
Benchmark for 50 000 000 prime numbers as single and multicore
...Added C files for gcc compiler in Windows 10 and for Xcode C command line project in MacOS ( tested on Mac mini M2 with single core 16 to 25 sec and multicore 2,3 to 5 second by compiler -O switch). Surprise, same code in JavaScript for M2 chip in Safari: 12,5 sec single core and 3,3 sec multi core. Python version with numba and numpy on MacOS with M2: 3,78 sec, Intel Ultra 5 225F Linux Fedora 43 GNOME(*Intel): 3,64 sec., W11Intel: 3,73; Faster style in python, MacOS M2: 1,81 sec, *Intel & W11Intel: 2,02 sec.; Ultra faster style in python, MacOS M2: 1,24 s - 1,26 s - 1,34 s, *Intel: 1,48 s - 1,50 s, W11Intel: 1,53 - 1,63. -
6
Python Data Science Handbook
Python Data Science Handbook: full text in Jupyter Notebooks
The Python Data Science Handbook is a comprehensive collection of Jupyter notebooks written by Jake VanderPlas covering fundamental Python libraries for data science, including IPython, NumPy, Pandas, Matplotlib, Scikit-Learn and more. The project is designed for data scientists, researchers, and anyone transitioning into Python-based data work; it assumes you already know basic Python and focuses more on how to use the ecosystem effectively. ... -
7
DeepMind Educational Resources
DeepMind's repo of educational notebooks for learning AI and research
Educational is an open collection of interactive tutorials created by Google DeepMind to make the fundamentals of machine learning and artificial intelligence accessible to learners of all backgrounds. The repository provides hands-on, beginner-friendly resources that introduce essential AI concepts through Google Colab notebooks, combining intuitive explanations with executable code. The tutorials cover a broad range of topics—from foundational Python programming and data handling to... -
8
Python Tutorials
Machine Learning Tutorials
Python Tutorials is a large set of educational tutorials focused on Python and related technologies, catering especially to learners who want hands-on examples and clear explanations. Created by an experienced instructor and educator, the repository covers a wide range of programming basics and advanced topics. This includes foundational Python concepts, data processing with libraries like NumPy and pandas, threading and multiprocessing for concurrency, and practical use of libraries such as Matplotlib for data visualization. ... -
9
Scikit-learn Tutorial
An introductory tutorial for scikit-learn
Scikit-learn Tutorial contains the materials for Jake VanderPlas’s introductory scikit-learn tutorial, originally used at major Python conferences. It provides a collection of notebooks that walk attendees from basic machine-learning concepts into practical modeling using the scikit-learn library. The tutorial covers data preparation, model fitting, evaluation, and common algorithms such as classification, regression, clustering, and dimensionality reduction. It is designed for people who already have a working Python environment and some familiarity with NumPy, SciPy, and Matplotlib. ... -
Earn up to 16% annual interest with Nexo.Put idle assets to work with competitive interest rates, borrow without selling, and trade with precision. All in one platform. Geographic restrictions, eligibility, and terms apply.
-
10
100 numpy exercises
100 numpy exercises (with solutions)
This is a collection of numpy exercises from numpy mailing list, stack overflow, and numpy documentation. I've also created some problems myself to reach the 100 limit. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercises for those who teach. For extended exercises, make sure to read From Python to NumPy. -
11
Python Machine Learning book
The book code repository and info resource
What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning. From theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano. This is not... -
12
Salstat2
statistical package designed for the end user, multiplatform
Salstat2 is an statistical package written in python and designed for the end user It has a graphical user interface and also it is scriptable, It's multiplatform, It has a graphic system inherited from matplotlib, It allows you to use different libraries like numpy - for numerical calculations, it also lets you to interact with Microsoft Excel (R) by using a com client under windows(R) platform and finally you can create your own dialogs by using the interactive shell or the script panel.
- Previous
- You're on page 1
- Next
