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Igel

A CLI tool to run machine learning without writing code
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What is Igel?

It is a delightful machine learning tool that allows to train, test and use models without writing code.
Igel is a tool in the Machine Learning Tools category of a tech stack.
Igel is an open source tool with GitHub stars and GitHub forks. Here’s a link to Igel's open source repository on GitHub

Igel Integrations

Igel's Features

  • Supports all state of the art machine learning models (even preview models)
  • Supports different data preprocessing methods
  • Provides flexibility and data control while writing configurations
  • Supports cross validation
  • Supports both hyperparameter search (version >= 0.2.8)
  • Supports yaml and json format
  • Supports different sklearn metrics for regression, classification and clustering
  • Supports multi-output/multi-target regression and classification
  • Supports multi-processing for parallel model construction

Igel Alternatives & Comparisons

What are some alternatives to Igel?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
CUDA
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
See all alternatives

Igel's Followers
3 developers follow Igel to keep up with related blogs and decisions.