Keras pretrained models (VGG16, InceptionV3, Resnet50, Resnet152) + Transfer Learning for predicting classes in the Oxford 102 flower dataset
Transfer Learning Suite in Keras. Perform transfer learning using any built-in Keras image classification model easily!
deep learning for image processing including classification and object-detection etc.
An integral project in Data Mining, mainly using Machine Learning.
A deep learning project written in PyTorch, intended as a comparison between a convolutional neural network, recurrent neural network and ConvNet + LSTM for image recognition on MNIST dataset.
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first identified in December 2019 in Wuhan, China, and has since spread globally, resulting in an ongoing pandemic. Long Short Term Memories(LSTMs) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDS's (intrusion detection systems). LSTMs can also be efficiently applied for time-series predictions. In this project, its shows a four stacked LSTM network for early prediction new Coronavirus dissease infections in some of the mentioned affected countries (India, USA, Czech Republic and Russia) , which is based on real world data sets which are analyzed using various perspectives like day-wise number of confirmed cases, number of Cured cases, death cases. This attempt has been done to help the concerned authorities to get some early insights into the probable devastation likely to be effected by the deadly pandemic.
Code used in Thesis "Convolutional Recurrent Neural Networks for Remaining Useful Life Prediction in Mechanical Systems".
