Rpa Libraries

16 Components & Libraries

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Rpa Libraries

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

To install the current release: Participating discussions via the following IM groups is also welcomed. Over the past few years, NNI has received thousands of feedbacks on GitHub issues, and pull req…

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data. Once TPOT is finished searching (or you get…

🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.

Get started with W&B in four steps: That's it! Navigate to the W&B App to view a dashboard of your first W&B Experiment. Use the W&B App to compare multiple experiments in a unified…

🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.

Use W&B to build better models faster. Track and visualize all the pieces of your machine learning pipeline, from datasets to production models. Flexible integration for any Python script: Build …

Automate browser-based workflows with LLMs and Computer Vision

Traditional approaches to browser automations required writing custom scripts for websites, often relying on DOM parsing and XPath-based interactions which would break whenever the website layouts ch…

[UNMAINTAINED] Automated machine learning for analytics & production

Automated machine learning for production and analytics auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on sing…

Collection of open-source libraries and tools for Robotic Process Automation (RPA), designed to be used with both Robot Framework and Python

Table of Contents The RPA Framework project currently includes the following libraries: To install all extra packages (including Playwright dependencies), you can use: Note Python 3.8 or higher is re…

Free SaaS boilerplate (Python/PostgreSQL/ReactJS/Webpack)

Currently, the boilerplate works with Python 3.5. All features/functions are put into separate structures, often front-end and back-end side-by-side. The whole project is divided into the following p…

Python library to easily log experiments and parallelize hyperparameter search for neural networks

Test tube is a python library to track and parallelize hyperparameter search for Deep Learning and ML experiments. It's framework agnostic and built on top of the python argparse API for ease of use.…

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

Automatically save and learn from Experiment results, leading to long-term, persistent optimization that remembers all your tests. HyperparameterHunter provides a wrapper for machine learning algorit…

🔧 Python Random Graph Generator

Pyrgg will likely run on a modern dual core PC. Typical configuration is: Note that it may run on lower end equipment though good performance is not guaranteed. Graph Line(.gl) GDF(.gdf) You can also…

State-of-the art Automated Machine Learning python library for Tabular Data

Classifier: Regression: DataPrepare: Simple Models Wrapper: More examples in the folder ./examples: It integrates many popular frameworks: Run

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

TPOT stands for Tree-based Pipeline Optimization Tool. TPOT2 is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. Consider TPOT2 your Data …

Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning

Here you find my IPython (Jupyter) notebooks from my blog (ipythonquant.wordpress.org). Feel free to download the samples and play around with them.

Implementation of Logistic Regression, MLP, CNN, RNN & LSTM from scratch in python. Training of deep learning models for image classification, object detection, and sequence processing (including transformers implementation) in TensorFlow.

The solutions uploaded in this repository are only for reference when you got stuck somewhere. Please don't use these solutions to pass programming assignments.

🔬 Some personal research code on analyzing CNNs. Started with a thorough exploration of Stanford's Tiny-Imagenet-200 dataset.

Using Anaconda is a GREAT way to keep everything consistentregardless of what machine you run your code on. The following command will create an Anaconda virtual environment with themodules listed in…

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