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Name: amir hossein fouladi
Type: User
Bio: M.Sc student in bio medical engineering (signal processing). Interested in AI especially Deep Learning.
Name: amir hossein fouladi
Type: User
Bio: M.Sc student in bio medical engineering (signal processing). Interested in AI especially Deep Learning.
This code implements an example of a CGAN deep learning model using PyTorch. The architecture used for the generator and discriminator is MLP (multi layer perceptron) network. This model is trained with MNIST dataset and finally it can generate images of numbers 0 to 9 according to the label we specify for it.
This code implements the EEG Net deep learning model using PyTorch. The EEG Net model is based on the research paper titled "EEGNet: A Compact Convolutional Neural Network for EEG-based Brain-Computer Interfaces".
In this notebook, we want to recognize the objects in the image using pre-trained models. The used models are fasterRCNN, RetinaNet, SSD and FCOS. The code is programmed in Python language and uses PyTorch and TorchVision library.
In this notebook, we aim to recognize speech commands using classification. For this purpose, we used the SPEECHCOMMANDS dataset and the deep convolutional model M5. The code is written in Python and designed for the PyTorch platform.
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