diaazg/Deep-learning
Deep learning notebooks and model implementations, exploring CNNs, RNNs, LSTMs, Transformers, and more during my AI learning journey.
This collection provides detailed notebooks and model implementations for anyone learning deep learning. It offers practical examples and experiments, showing how neural networks are built, trained, and applied to real-world data like images and sequences. If you're studying AI, this resource helps you understand core concepts and gain hands-on experience with various deep learning architectures.
Use this if you are a student or self-learner in artificial intelligence looking for practical code examples to understand and implement deep learning models from scratch.
Not ideal if you are looking for a pre-built, production-ready solution to solve a specific business problem, as this repository focuses on learning and experimentation.
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Jupyter Notebook
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Last pushed
Oct 20, 2025
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