Welcome to my collection of code for attempting to create deep learning models. This repository contains various experiments, architectures, and techniques related to building deep learning models across different domains. The aim of this project is to explore and implement various neural network architectures and document the learning process.
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Neural Network Architectures: Various deep learning models, including but not limited to:
- Feedforward Neural Networks (FNN)
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Long Short-Term Memory (LSTM)
- Transformer models
- Autoencoders
- Generative Adversarial Networks (GANs)
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Projects: Collection of applied projects showcasing the use of different architectures for solving real-world problems.
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Experimentation: Notebook files containing various trials with different configurations, hyperparameters, and datasets.