Pytorch
Pandas
Pytorch | Pandas | |
---|---|---|
381 | 420 | |
86,466 | 44,463 | |
1.2% | 0.7% | |
10.0 | 9.9 | |
3 days ago | 5 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Pytorch
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Must-Know 2025 Developer’s Roadmap and Key Programming Trends
Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python, try projects that combine data with everyday problems. For example, build a simple recommendation system using Pandas and scikit-learn.
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Decorator JITs: Python as a DSL
Basically this style of code - https://github.com/pytorch-labs/attention-gym/pull/84/files - has issues like this - https://github.com/pytorch/pytorch/pull/137452 https://github.com/pytorch/pytorch/issues/144511 https://github.com/pytorch/pytorch/issues/145869
For some higher level context, see https://pytorch.org/blog/flexattention/
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Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis.
- PyTorch 2.6.0 Release
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Responsible Innovation: Open Source Best Practices for Sustainable AI
Open source frameworks like PyTorch are already enabling Machine Learning breakthroughs because they’re living communities where great things happen through:
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Golang Vs. Python Performance: Which Programming Language Is Better?
- Data Science and AI: TensorFlow, PyTorch and scikit-learn are only a few of the standard Python libraries. - Web Development: development of web-based applications is made simple by frameworks such as Flask as well as Django. - Prototyping: Python's ease of use lets you quickly iterate and testing concepts.
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How to resolve the dlopen problem with Nvidia and PyTorch or Tensorflow inside a virtual env
By chance, Tensorflow or PyTorch can work with pip packages from Nvidia.
- Making VLLM work on WSL2
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2025’s Must-Know Tech Stacks
PyTorch
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Experiments with Byte Matrix Multiplication
> It's quite common in machine learning operations to multiply a matrix of unsigned byte by a matrix of signed byte. Don't ask me why, but that's the case.
Overflow is the reason. Intel's vpmaddubsw takes int8_t and uint8_t to give you results in int16_t. If both are unsigned 255 * 255 = 65025 will be out of range for int16_t so likely the instruction is designed to take int8_t and uint8_t. The overflow (or rather saturation with this instruction) can still occur because it sums to adjacent multiplication. See my comment in PyTorch. https://github.com/pytorch/pytorch/blob/a37db5ae3978010e1bb7...
Pandas
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Must-Know 2025 Developer’s Roadmap and Key Programming Trends
Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python, try projects that combine data with everyday problems. For example, build a simple recommendation system using Pandas and scikit-learn.
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Sample Super Store Analysis Using Python & Pandas
This tutorial provides a concise and foundational guide to exploring a dataset, specifically the Sample SuperStore dataset. This dataset, which appears to originate from a fictional e-commerce or online marketplace company's annual sales data, serves as an excellent example for learning and how to work with real-world data. The dataset includes a variety of data types, which demonstrate the full range of capabilities that the Pandas library, written in Python can offer.
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Bullish on AI infrastructure, bearish on AI developer frameworks
Data preprocessing and manipulation: Libraries like Pandas solve for the messy, real-world challenge of efficiently wrangling and cleaning large datasets. Without it, you'd be reinventing functionality for basic tasks like merging, filtering, or aggregating data.
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Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis.
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Building a Sarcasm Detection System with LSTM and GloVe: A Complete Guide
Pandas
- Fixing timestamp overflow error in Python
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Build a Competitive Intelligence Tool Powered by AI
Add data visualization to make it actionable for your business using pandas.pydata.org and matplotlib.org
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How to Use Lambda Functions in Python
In this tutorial, we'll see how to use Lambda functions with the library Pandas: a fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation library. If you don't have it installed, run the following:
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Using Polars in Rust for high-performance data analysis
One of the main selling points of Polars over similar solutions such as Pandas is performance. Polars is written in highly optimized Rust and uses the Apache Arrow container format.
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What AI/ML Models Should You Use and Why?
Most ML libraries, like scikit-learn, pandas, etc., allow you to visualize and predict the linear relationship between the variables in the training data. Since linear regression is a simple model, it is easy to explain the output and can be used in industries requiring explainable solutions.
What are some alternatives?
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
tensorflow - An Open Source Machine Learning Framework for Everyone
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Deep Java Library (DJL) - An Engine-Agnostic Deep Learning Framework in Java
NumPy - The fundamental package for scientific computing with Python.
CNTK - Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
SymPy - A computer algebra system written in pure Python