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Caffe

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Theano

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Theano vs Caffe: 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, Caffe is detailed as "A deep learning framework". It is a deep learning framework made with expression, speed, and modularity in mind.

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

Theano and Caffe are both open source tools. Caffe with 29.2K GitHub stars and 17.6K forks on GitHub appears to be more popular than Theano with 8.94K GitHub stars and 2.51K GitHub forks.

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What is Caffe?

It is a deep learning framework made with expression, speed, and modularity in mind.

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 are some alternatives to Caffe and Theano?
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.
Torch
It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
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.
Caffe2
Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
See all alternatives