we show a universal algorithm can detect most types of trauma-related radiographic findings on PXRs. We develop a multiscale deep learning algorithm called PelviXNet trained with 5204 PXRs with weakly supervised point annotation. PelviXNet yields an area under the receiver operating characteristic cur...
Deep learning isn’t a new brand. Actually, it is derived from the concept of neural networks. The main model used in deep learning is called deep neural networks (DNNs). Thus, neural network is the precursor of deep learning. In 1980s, Hinton and other researchers helped to revive the ...
The most popular and primary approach of deep learning is using Artificial neural network (ANN). They are inspired from the model of human brain, which is the most complex organ of our body. The human brain is made up of more than 90 billion tiny cells called Neurons. Neurons are inter-...
先介绍一个概念, 即 Objective function or criterion: Most deep learning algorithms involveoptimizationof some sort. However, we called the function as Objective function if its for maximize or minimize. When we are minimizing it, we may also call it the cost function, loss function, or error f...
We propose deep learning-based algorithms to monitor the driver’s emotional state even when emotions are not fully revealed by facial expressions while driving. Our emotion recognition system that monitors the driver’s real emotions is called DRER. We propose the two main steps to recognize the...
in-depth learning became essential for machine learning practitioners and even for many software engineers. This book provides a wide range of role for data scientists and software engineers with experience in machine learning. You will start with the basics of deep learning and quickly move on to...
Another process called backpropagationuses algorithms, such as gradient descent, to calculate errors in predictions, and then adjusts the weights and biases of the function by moving backwards through the layers to train the model. Together, forward propagation and backpropagation enable a neural netw...
Deep learning is a subset ofmachine learning (ML)that usesneural networkswith many layers, known as deep neural networks (DNNs). These networks consist of numerous interconnected units called neurons or nodes that act as feature detectors. Each neural network has an input layer to receive data,...
Introduced back in 2015 by a team of Google engineers, the concept of deep dreaming has given another dimension to the realm of deep learning. Deep dreaming involves feeding algorithms to machines, which can then mimic the process of dreaming in human neural networks. A website called Deep Dre...
Additionally, neural networks are structured so that the neural network can continue to learn and make intelligent decisions all on its own. Machine learning models, on the other hand, are limited to decision-making based only on what it has specifically been trained on. ...