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Igel vs Keras: What are the differences?
What is Igel? A CLI tool to run machine learning without writing code. It is a delightful machine learning tool that allows to train, test and use models without writing code.
What is Keras? Deep Learning library for Theano and TensorFlow. Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/.
Igel and Keras can be categorized as "Machine Learning" tools.
Some of the features offered by Igel are:
- 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
On the other hand, Keras provides the following key features:
- neural networks API
- Allows for easy and fast prototyping
- Convolutional networks support
Keras is an open source tool with 50K GitHub stars and 18.7K GitHub forks. Here's a link to Keras's open source repository on GitHub.
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Learn MorePros of Igel
Pros of Keras
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Pros of Keras
- Quality Documentation8
- Supports Tensorflow and Theano backends7
- Easy and fast NN prototyping7
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Cons of Igel
Cons of Keras
Cons of Igel
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Cons of Keras
- Hard to debug4
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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.
What is Keras?
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
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What are some alternatives to Igel and Keras?
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.
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.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
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.
Streamlit
It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.