Build the deep learning applications
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Updated
Dec 27, 2018
Build the deep learning applications
simple implementations of resnet and densenet for the purpose of practice
Classify Lymphoma using Pytorch and DenseNet
In this research work, unsupervised abnormality has been detected by using intelligent and heterogeneous autonomous systems.
Automated White Matter Hyperintensities (WMH) segmentation using Dense U-Net for improved MRI analysis in neurodegenerative disease research
Re-training the deep neural network DenseNet using various learning rate strategies. Entry for the Food Recognition Challenge for the Master's course Applied Machine Learning.
Experimented with and compared DFW neural network optimizer with SGD and ADAM on both vision and language tasks
TUM - Advanced Deep Learning for Robotics SS21 project
Covid-19 Detection using is MobileNetV2, ResNet50, DenseNet169 and Xception which supported by sklearn library to train and testing on the dataset which consists of 3 classes, which are COVID-19, Normal and Lung Opacity.
A barebone introduction & practice of JAX
COVID-19 Chest X-Ray Prediction using Deep Learning
This notebook is the solution for Kaggle's Petals to the Metal - Flower Classification on TPU challenge: https://www.kaggle.com/c/tpu-getting-started
Comparing Classifiers including GridSearch for Survival on Titanic based on features like gender, fare and age. Comparison via self written ScoreAggregator class.
A PyTorch Implementation for Densely Connected Convolutional Networks (DenseNets)
Deploying a Cotton Plant Disease Classification Flask application Using DenseNet121
Implementations of popular deep learning research papers
In depth study the effect of varying occlusion proportions and nature on the performance of CNNmodels by fine tuning and training these from scratch
-Classification disease on OCT images
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