GitHub - JesusMda/DeepLearning-Course-2024: Developed deep learning models in Python as part of graduate coursework. Covered core topics including backpropagation, autoencoders, transfer learning, recurrent and convolutional neural networks (RNNs and CNNs), as well as architectures such as U-Net and Pix2Pix applied to depth estimation tasks.

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Developed deep learning models in Python as part of graduate coursework. Covered core topics including backpropagation, autoencoders, transfer learning, recurrent and convolutional neural networks (RNNs and CNNs), as well as architectures such as U-Net and Pix2Pix applied to depth estimation tasks.

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Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
.ipynb_checkpoints		.ipynb_checkpoints
.virtual_documents		.virtual_documents
Autoencoder_BackPropagation.ipynb		Autoencoder_BackPropagation.ipynb
Autoencoder_BackPropagation.pdf		Autoencoder_BackPropagation.pdf
Autoencoder_Fine-tuning.ipynb		Autoencoder_Fine-tuning.ipynb
Autoencoder_Fine-tuning.pdf		Autoencoder_Fine-tuning.pdf
Pix2Pix.ipynb		Pix2Pix.ipynb
Pix2Pix.pdf		Pix2Pix.pdf
Recurrent_Neural_Network.ipynb		Recurrent_Neural_Network.ipynb
Recurrent_Neural_Network.pdf		Recurrent_Neural_Network.pdf
UNET.ipynb		UNET.ipynb
UNET.pdf		UNET.pdf