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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. "Keras 3 is a full rewrite of Keras [and can be used] as a low-level cross-framework language to develop custom components such as layers ...
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] Caffe supports GPU- and CPU-based acceleration computational kernel libraries such as Nvidia cuDNN and Intel MKL. [9] [10]
[19] [20] [21] A slow neural network learns by gradient descent to generate keys and values for computing the weight changes of the fast neural network which computes answers to queries. [17] This was later shown to be equivalent to the unnormalized linear Transformer. [22] A follow-up paper developed a similar system with active weight ...
Apache MXNet is an open-source deep learning software framework that trains and deploys deep neural networks. It aims to be scalable, allows fast model training, and supports a flexible programming model and multiple programming languages (including C++, Python, Java, Julia, MATLAB, JavaScript, Go, R, Scala, Perl, and Wolfram Language).
MXNet: an open-source deep learning framework used to train and deploy deep neural networks. PyTorch : Tensors and Dynamic neural networks in Python with GPU acceleration. TensorFlow : Apache 2.0-licensed Theano-like library with support for CPU, GPU and Google's proprietary TPU , [ 116 ] mobile
ML.NET is a free software machine learning library for the C# and F# programming languages. [4] [5] [6] It also supports Python models when used together with NimbusML.The preview release of ML.NET included transforms for feature engineering like n-gram creation, and learners to handle binary classification, multi-class classification, and regression tasks. [7]
An echo state network (ESN) [1] [2] is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer (with typically 1% connectivity). The connectivity and weights of hidden neurons are fixed and randomly assigned.
HDC algorithms can replicate tasks long completed by deep neural networks, such as classifying images. [2] Classifying an annotated set of handwritten digits uses an algorithm to analyze the features of each image, yielding a hypervector per image.