The performance of both CNN and machine learning methods was evaluated using accuracy, precision, sensitivity, specificity, and F-score metrics. To optimize classification performance and reduce computational cost, the RelieF algorithm was used to select the best 5 out of 6 features. Compared to ...
However, in other cases, the two types of models can complement each other. Combining CNNs' spatial processing andfeature extractionabilities with RNNs' sequence modeling and context recall can yield powerful systems that take advantage of each algorithm's strengths. For example, a CNN and an R...
YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers https://arxiv.org/abs/1811.05588 AttentionNet: Aggregating Weak Directions for Accurate Object Detection intro: ICCV 2015 intro: state-of-the-art performance of 65% (AP) on PASCAL VOC 2007/2012 human detection ...
nlpmachine-learningneural-networktensorflowsvmgenetic-algorithmlinear-regressionregressioncnnodeclassificationrnntensorboardpacktpubtensorflow-cookbooktensorflow-algorithmskmeans-clustering UpdatedMay 23, 2024 Jupyter Notebook Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-...
Convolutional neural networks (CNNs) and generative adversarial networks (GANs) are examples ofneural networks-- a type of deep learning algorithm modeled after how the human brain works. CNNs, one of the oldest and most popular of thedeep learningmodels, were introduced in the 1980s and are...
A Convolutional Neural Network (CNN), also known as ConvNet, is a specialized type of deep learning algorithm mainly designed for tasks that necessitate object recognition, including image classification, detection, and segmentation. CNNs are employed in a variety of practical scenarios, such as aut...
algorithm uses Stochastic Gradient Descent with Momentum (SGDM) with an initial learning rate of 0.001. During training, the initial learning rate is reduced every 8 epochs (1 epoch is defined as one complete pass through the entire training data set). The training algorithm is run for 40 ...
R-CNN depends on the Selective Search algorithm for generating region proposals, which takes a lot of time. Moreover, this algorithm cannot be customized to the detection problem. Each region proposal is fed independently to CNN for feature extraction, making it impossible to run R-CNN in real...
YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers https://arxiv.org/abs/1811.05588 AttentionNet: Aggregating Weak Directions for Accurate Object Detection intro: ICCV 2015 intro: state-of-the-art performance of 65% (AP) on PASCAL VOC 2007/2012 human detection ...
R-CNN depends on the Selective Search algorithm for generating region proposals, which takes a lot of time. Moreover, this algorithm cannot be customized to the detection problem. Each region proposal is fed independently to CNN for feature extraction, making it impossible to run R-CNN in real...