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C++: Graphical user interface: Yes No Yes No Analytical differentiation No No No No Yes Yes OpenNN: Artelnics 2003 GNU LGPL: Yes Cross-platform: C++: C++: Yes No Yes No ? ? No No No ? Yes PlaidML: Vertex.AI, Intel: 2017 Apache 2.0: Yes Linux, macOS, Windows: Python, C++, OpenCL: Python, C++? Some OpenCL ICDs are not recognized No No Yes Yes Yes ...
Pattern recognition is the task of assigning a class to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess (PR) capabilities but their primary function is to distinguish and create emergent patterns.
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 ...
Differentiable programming has found use in a wide variety of areas, particularly scientific computing and machine learning. [5] One of the early proposals to adopt such a framework in a systematic fashion to improve upon learning algorithms was made by the Advanced Concepts Team at the European Space Agency in early 2016.
A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topological order.
The following code-block defines a neural network with linear layers using the nn module. import torch from torch import nn # Import the nn sub-module from PyTorch class NeuralNetwork ( nn . Module ): # Neural networks are defined as classes def __init__ ( self ): # Layers and variables are defined in the __init__ method super () . __init__ ...
A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]
Gradient-based one-side sampling (GOSS) is a method that leverages the fact that there is no native weight for data instance in GBDT. Since data instances with different gradients play different roles in the computation of information gain, the instances with larger gradients will contribute more to the information gain.