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This is a framework for scientific computing. For LuaJIT
This is similar to PyTorch, Torch is a Tensor library which is similar to NumPy and it supports the GPU. Unlike PyTorch, Torch is wrapped in LuaJIT, with an underlying C/CUDA implementation.
This framework Torch prioritizes speed, flexibility, and simplicity for building the algorithm. With the neural networks which are leading and the libraries of optimization, Torch provides users with libraries, that are easy to use while accessing flexible implementation of complex neural network topologies.
Scikitlearn is an open source, commercially usable AI library. Another Python library, sci-kit learn supports both supervised and unsupervised machine learning. Specifically, it supports classification, regression, and clustering algorithms, as well as dimensionality reduction, model selection, and preprocessing.
Scikitlearn markets itself as simple and efficient tools for data mining and data analysis that is accessible to everybody, and reusable in various contexts.
To support these claims, sci-kit learn offers an extensive user guide so that data scientists can quickly access resources on anything from multiclass and multilabel algorithms to covariance estimation.
A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently
Theano is a Python library and optimizing compiler designed for manipulating and evaluating expressions. In particular, Theano evaluates matrix-valued expressions.
Speed is one of Theano's strongest suits. It can compete toe-to-toe with the speed of handcrafted C language implementations that involve a lot of data. By taking advantage of recent GPUs, Theano has also been able to top C on a CPU by a significant degree.
Even though new development has ceased for Theano, its still a powerful and efficient platform for deep learning.
Caffe is the framework of deep learning which is developed by Berkeley AI research which is in collaboration with the contributors of community, and it offers both models and worked examples for deep learning.
Caffe prioritizes expression, speed, and modularity in its framework. In fact, its architecture supports configuration defined models and optimization without hard coding, as well as the ability to switch between CPU and GPU.
Plus, Caffe is highly adaptive to research experiments and industry deployments because it can process over 60M images per day with a single NVIDIA K40 GPU one of the fastest convnet implementations available, according to Caffe.
This is a high-level network API which helps to run on the TensorFlows peak, Microsoft Cognitive Toolkit, or Theano. Through this deep learning of Python, the library facilitates fast experimentation which claims that the key to good research is being able to move from good result to the idea.
Instead of an end-to-end machine learning framework, Keras operates as a user-friendly, easily extensible interface that supports modularity and total expressiveness. Standalone modules such as neural layers, cost functions, and more can be combined with few restrictions, and new modules are easy to add.