Usually, the effectiveness of an ML algorithm is highly dependent on the integrity of the input-data representation. It has been shown that a suitable data representation provides an improved performance when compared to a poor data representation. Thus, a significant research trend in ML for many...
are typically involves the following steps before starting the processing. First the image that needs to be processed must be obtained and saved in the appropriate directory. The algorithm then finds the white pixels in the image computes their non-zero (nnz) value and compares it with the give...
A convolutional neural network is made up of numerous layers, such as convolution layers, pooling layers, and fully connected layers, and it uses a backpropagation algorithm to learn spatial hierarchies of data automatically and adaptively. You will learn more about these terms in the following sec...
In this post, we’ll learn how to implement a Convolutional Neural Network (CNN) from scratch using Keras. Here, we show a CNN architecture similar to the structure of VGG-16 but with fewer layers. We ... Tags: cnn convolutional neural network deep learning Keras tensorflow...
TNN supports INT8 WINOGRAD algorithm, (input 6bit), further reduces the model calculation complexity without sacrificing the accuracy. TNN supports mixed-precision data in one model, speeding up the model's calculation speed while preserving its accuracy. ...
algorithm for neural networks. However, using different (and mostly novel) techniques, each of these three groups was able to get these early layers to learn useful representations. This resulted in much more powerful neural networks. A full description of their contributions is beyond the scope ...
2.2. Deep learning methods With the rapid advancement of deep learning technology, deep neural networks have been increasingly adopted for seizure detection tasks. For instance, Yan et al. (2019) proposed an automated seizure detection algorithm based on CNN that utilized the EEG spectrum as the ...
The algorithm imitates white sharks, which use their superior perceptual abilities to sense complex information while hunting in the deep sea 33. The basic idea of the white shark optimization algorithm is to model and simulate three primary behaviors: rapid movement of white sharks towards prey, ...
a machine learning algorithm training on 2K x 2K images would be forced to find 4M separate weights. Thanks to convolutions, a machine learning algorithm only has to find weights for every cell in theconvolutional filter, dramatically reducing the memory needed to train the model. When the conv...
TNN supports INT8 WINOGRAD algorithm, (input 6bit), further reduces the model calculation complexity without sacrificing the accuracy. TNN supports mixed-precision data in one model, speeding up the model's calculation speed while preserving its accuracy. ...