Advanced and Predictive Analytics


OpenNN is an open source class library written in C++ which implements neural networks, a main area of machine learning research. OpenNN is designed for researchers and developers with advanced understanding of artificial intelligence. Its key features include deep architectures and fast performance. It has been successfully applied to many different analytics tasks in industries such as energy, manufacturing, logistics, marketing, healthcare, etc. Extensive documentation is available on the website, including an introductory tutorial that explains the basics of neural networks.


Managed by a company called Numenta, NuPIC is an open source artificial intelligence project based on a theory called Hierarchical Temporal Memory, or HTM. Essentially, HTM is an attempt to create a computer system modeled after the human neocortex. The goal is to create machines that "approach or exceed human level performance for many cognitive tasks." NuPIC contains a library that provides the building blocks for online prediction systems.


Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala, designed to be used in business environments. DL4j is integrated with Hadoop and Apache Spark, and brings AI to business environments for use on distributed GPUs and CPUs. Deeplearning4j aims to be cutting-edge plug and play, more convention than configuration, which allows for fast prototyping for non-researchers.


CNTK (Computational Network Toolkit) by Microsoft Research, referred to The Microsoft Cognitive Toolkit, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix operations upon their inputs. CNTK allows to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs).


Caffe is a deep learning framework and this tutorial explains its philosophy, architecture, and usage. This is a practical guide and framework introduction, so the full frontier, context, and history of deep learning cannot be covered here. While explanations will be given where possible, a background in machine learning and neural networks is helpful.


Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, and more


Fast Scalable Machine Learning API For Smarter Applications (Deep Learning, GBM, GLM...)

H2O scales statistics, machine learning, and math over Big Data.

H2O uses familiar interfaces like R, Python, Scala, the Flow notebook graphical interface, Excel, & JSON so that Big Data enthusiasts & experts can explore, munge, model, and score datasets using a range of algorithms including advanced ones like Deep Learning. H2O is extensible so that developers can add data transformations and model algorithms of their choice and access them through all of those clients.


Tensors and Dynamic neural networks in Python with strong GPU acceleration


Deep Learning library for Python. Convnets, recurrent neural networks, and more.
Keras is a minimalist, highly modular neural networks library, written in Python and capable of running either on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation.


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.


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