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AutoGluon

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Theano

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Theano vs AutoGluon: What are the differences?

Developers describe Theano as "Define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently". Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray). Using Theano it is possible to attain speeds rivaling hand-crafted C impleme. On the other hand, AutoGluon is detailed as "*AutoML Toolkit for Deep Learning *". It automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on image, text, and tabular data.

Theano and AutoGluon belong to "Machine Learning Tools" category of the tech stack.

Theano and AutoGluon are both open source tools. Theano with 9.05K GitHub stars and 2.52K forks on GitHub appears to be more popular than AutoGluon with 1.72K GitHub stars and 193 GitHub forks.

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What is AutoGluon?

It automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on image, text, and tabular data.

What is Theano?

Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).

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    What tools integrate with AutoGluon?
    What tools integrate with Theano?
    What are some alternatives to AutoGluon and Theano?
    XGBoost
    Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow
    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.
    Keras
    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
    See all alternatives