1D CNN VAE on pytorch for MNIST Dataset

• Complete the code for 1D CNN Variational autoencoder (1D-CNN VAE) using a notebook as seen in VAE_pytorch_custom notebook in the attached.

• Write and comment the meaning of the input of a 1D CNN and others used in pytorch and use the MNIST dataset for it.

• Plot the 2D latent space generated by training a 1D CNN VAE and ensure the latent space corresponds to that obtained for 1D CNN VAE of tensorflow (see attached).

• The first notebook is done for tensorflow and you can use ideas of the network structure for tensorflow to design your pytorch version.

Evner: Pytorch, Machine Learning (ML), Kunstig intelligens, Datasøgning, Datavidenskab

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Om arbejdsgiveren:
( 1 bedømmelse ) GALAXY ZERO, France

Projekt ID: #31046502

6 freelancere byder i gennemsnit €23 timen for dette job


I am Nagaraju and I am currently working as a Visiting Researcher at Mirai Research Innovation Institue, Japan. and having 5 + years of experience in Machine Learning, Deep Learning, Computer Vision, and Natural Langua Flere

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Hi, I hope you are doing fine. I have done many image processing and video processing projects in Matlab, Python, JAVA, etc. I already have worked with some well-known models like, YOLO, RCNN, Fast-RCNN, Faster-RCNN a Flere

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Hey I hope you are doing fine I am a data scientist and have vast skills in deep learning and it’s libraries like tf pytorch etc I can help you with this project Kindly allow me to do this project and I will not dis Flere

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Hello this is Collins and am a Machine Learning expert, I have gone through your project and I will be able to perform the following; • Write and comment the meaning of the input of a 1D CNN and others used in pytorch Flere

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I have extensively worked with CAE and VAEs and I am very efficient with PyTorch as well! Would love to help you out with the migration of code from Tensorflow to PyTorch with well commented code.

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Hi, I'm student researcher in deep learning and have fair amount of experience in the workings of deep neural networks. I've worked with Vanilla-VAE, Conditional-VAE, and Beta-VAE, in the past. I've also worked on Baye Flere

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