Opens Source AI Frameworks

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Artificial intelligence (AI) frameworks play an important part in machine learning. There are more than a dozen open-source AI frameworks available to data scientists and engineers in the market. Google TensorFlow is one of the more popular ones. It comes feature-packed with tools, libraries, pre-packaged models, and more.

Other popular frameworks include PyTorch and Caffe. One of the challenges is determining which one is the best fit for a particular use case. Use cases vary widely. Image classification, computer vision, anomaly detection are just a few of many. PyTorch, is a solid alternative for TensorFlow, depending on the use case. Scikit-learn works well in data analysis and data mining. Facebook, the founder of PyTorch continues to invest in it, as it plays an important part in the technology stack.

Here are ten popular open-source artificial intelligent frameworks.

FrameworkLanguageTypeUse Cases
Accord.NetC++Classification, regression, and clusteringAudio. Image processing. Computer vision.
Apache MahoutJava / ScalaDistributed linear algebra frameworkIdeal for mathematicians and statisticians.
CaffeC++Deep LearningImage classification. Speech. Multimedia.
KerasPythonDeep LearningHigh-level API. Tightly integrated with TensorFlow.
MLPackC++Swiss army knife” of methods and functionsSupports NeighborSearch. K-Means, and RangeSearch.
PyTorchPythonDynamic neural nets and Tensor computation2nd most popular framework. Developed by Facebook.
Scikit-learnPythonClassification, regression, and clusteringData mining. Data analysis. Developed SciPy, NumPy, and motplotlib.
Spark MLibJava, Scala, Python, and RClassification, regression, clustering, and more9x faster than Mahout. Used for matching learning pipelines.
TorchLua / LuaJITDeep LearningSupports multi-dimensional arrays. GPUs ready. Computer vision. Image classification. Audio, and video.
TheanoPythonUsed to evaluate mathematical expressionsIntegrated with NumPy. Supports multi-dimensional arrays. Supports GPU’s.

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