i have used 6 defect types which are common in pipeline defect detection.
-
Updated
May 29, 2024 - Python
i have used 6 defect types which are common in pipeline defect detection.
Learning Rate Warmup in PyTorch
Radio interferometric calibration with PyTorch. An example of how to solve a general optimization problem.
Differentially Private Gradient Descent Optimizers. DA204 Course Project
Software of Development using AI with Neural Network and Python
Partially Adaptive Momentum Estimation method in the paper "Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks" (accepted by IJCAI 2020)
Tensorflow-Keras callback implementing arXiv 1712.07628
Implementation of optimization and regularization algorithms in deep neural networks from scratch
implementation of neural network from scratch only using numpy (Conv, Fc, Maxpool, optimizers and activation functions)
Example of the implementation of Adam Gradient Descent optimization algorithm on a simple convex function.
RAdam implemented in Keras & TensorFlow
Comprehensive image classification for training multilayer perceptron (MLP), LeNet, LeNet5, conv2, conv4, conv6, VGG11, VGG13, VGG16, VGG19 with batch normalization, ResNet18, ResNet34, ResNet50, MobilNetV2 on MNIST, CIFAR10, CIFAR100, and ImageNet1K.
Classification of data using neural networks — with back propagation (multilayer perceptron) and with counter propagation
On the Variance of the Adaptive Learning Rate and Beyond
Add a description, image, and links to the adam topic page so that developers can more easily learn about it.
To associate your repository with the adam topic, visit your repo's landing page and select "manage topics."