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SqueezeNet. In computer vision, SqueezeNet is the name of a deep neural network for image classification that was released in 2016. SqueezeNet was developed by researchers at DeepScale, University of California, Berkeley, and Stanford University. In designing SqueezeNet, the authors' goal was to create a smaller neural network with fewer ...
Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at University of California, Berkeley. It is open source, under a BSD license. [4] It is written in C++, with a Python interface. [5]
The Fashion MNIST dataset is a large freely available database of fashion images that is commonly used for training and testing various machine learning systems. Fashion-MNIST was intended to serve as a replacement for the original MNIST database for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits.
Time delay neural network. TDNN diagram. Time delay neural network ( TDNN) [1] is a multilayer artificial neural network architecture whose purpose is to 1) classify patterns with shift-invariance, and 2) model context at each layer of the network. Shift-invariant classification means that the classifier does not require explicit segmentation ...
Independent filmmaking is no easy task — even if you’re Steven Spielberg’s daughter. Destry Allyn Spielberg is set to make her feature directorial debut with “Please Don’t Feed the ...
TensorFlow.nn is a module for executing primitive neural network operations on models. [38] Some of these operations include variations of convolutions (1/2/3D, Atrous, depthwise), activation functions ( Softmax, RELU, GELU, Sigmoid, etc.) and their variations, and other operations ( max-pooling, bias-add, etc.).
Stable Diffusion is a latent diffusion model, a kind of deep generative artificial neural network. Its code and model weights have been released publicly, [8] and it can run on most consumer hardware equipped with a modest GPU with at least 4 GB VRAM.
Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections.