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
Densely Connected Convolutional Networks (DenseNets) with Tensorflow
An image classifier from scratch that will identify different species of flowers.
Classifying a given input image into 2 categories i.e. if it is the image of a cat or a dog using convolutional neural networks and deep learning implemented in tensorflow.
Cataract detection model
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
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