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Keras is an open-source library that provides a Python interface for artificial neural networks.Keras was first independent software, then integrated into the TensorFlow library, and later supporting more.
Caffe supports many different types of deep learning architectures geared towards image classification and image segmentation.It supports CNN, RCNN, LSTM and fully-connected neural network designs. [8]
These models are compressed and optimized in order to be more efficient and have a higher performance on smaller capacity devices. [64] TensorFlow Lite uses FlatBuffers as the data serialization format for network models, eschewing the Protocol Buffers format used by standard TensorFlow models. [64]
It supports full-fledged interfaces for training in C++ and Python and with additional support for model inference in C# and Java. TensorFlow: Apache 2.0-licensed Theano-like library with support for CPU, GPU, Google's proprietary tensor processing unit (TPU), [164] and mobile devices. Theano: The reference deep-learning library for Python with ...
It works on Linux, Windows, macOS, and is available in Python, [8] R, [9] and models built using CatBoost can be used for predictions in C++, Java, [10] C#, Rust, Core ML, ONNX, and PMML. The source code is licensed under Apache License and available on GitHub. [6] InfoWorld magazine awarded the library "The best machine learning tools" in 2017.
Dask is an open-source Python library for parallel computing.Dask [1] scales Python code from multi-core local machines to large distributed clusters in the cloud. Dask provides a familiar user interface by mirroring the APIs of other libraries in the PyData ecosystem including: Pandas, scikit-learn and NumPy.
Inception [1] is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1).). The series was historically important as an early CNN that separates the stem (data ingest), body (data processing), and head (prediction), an architectural design that persists in all modern
Probabilistic programming (PP) is a programming paradigm based on the declarative specification of probabilistic models, for which inference is performed automatically. [1] Probabilistic programming attempts to unify probabilistic modeling and traditional general purpose programming in order to make the former easier and more widely applicable.