Keras
Pandas

Keras | Pandas | |
---|---|---|
85 | 421 | |
62,562 | 44,513 | |
0.5% | 0.9% | |
9.9 | 9.9 | |
2 days ago | 7 days ago | |
Python | Python | |
Apache License 2.0 | 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.
Keras
-
Building a Sarcasm Detection System with LSTM and GloVe: A Complete Guide
Keras API reference
- Submitting GPU jobs to Slurm @ Loyola University Chicago
-
Top 8 OpenSource Tools for AI Startups
Star on GitHub ⭐ - Keras
-
Top 5 Production-Ready Open Source AI Libraries for Engineering Teams
At its heart is TensorFlow Core, which provides low-level APIs for building custom models and performing computations using tensors (multi-dimensional arrays). It has a high-level API, Keras, which simplifies the process of building machine learning models. It also has a large community, where you can share ideas, contribute, and get help if you are stuck.
-
Las 10 Mejores Herramientas de Inteligencia Artificial de Código Abierto
(https://dev-to-uploads.s3.amazonaws.com/uploads/articles/92cup4lywcjfq83xg0ea.png)
-
Using Google Magika to build an AI-powered file type detector
The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models.
- Side Quest #3: maybe the real Deepfakes were the friends we made along the way
-
Library for Machine learning and quantum computing
Keras
-
My Favorite DevTools to Build AI/ML Applications!
As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development.
- Release: Keras 3.3.0
Pandas
-
We Are Destroying Software
They are when the only reason they are flagged as security updates is because some a single group deems a very rare, obscure edge case as a HIGH severity vuln when in practice it rarely is => this leads to having to upgrade a minor version of a library that ends up causing breaking changes.
This is the recent thread I'm down. Pandas 2.2 broke SQLalchemy backwards compatibility: https://stackoverflow.com/questions/38332787/pandas-to-sql-t... + https://github.com/pandas-dev/pandas/issues/57049#issuecomme...
-
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.
-
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.
-
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.
-
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.
-
Building a Sarcasm Detection System with LSTM and GloVe: A Complete Guide
Pandas
- Fixing timestamp overflow error in Python
-
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
-
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:
-
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.
What are some alternatives?
scikit-learn - scikit-learn: machine learning in Python
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
tensorflow - An Open Source Machine Learning Framework for Everyone
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
PyBrain
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
NumPy - The fundamental package for scientific computing with Python.
