This repository documents a hands-on deep learning journey focused on from-scratch implementations and practical experiments. It covers core concepts such as neural networks, backpropagation, and optimization, and extends to advanced topics including CNNs, GANs, VAEs, attention mechanisms, and Transformer architectures, bridging theory and practice.
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A collection of my deep learning learning journey - implementations from scratch covering core concepts like neural networks, activation functions, gradient descent, and backpropagation. Includes hands-on experiments and training exercises on various datasets to understand how deep learning works under the hood.
asishjose/Deep-Learning
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A collection of my deep learning learning journey - implementations from scratch covering core concepts like neural networks, activation functions, gradient descent, and backpropagation. Includes hands-on experiments and training exercises on various datasets to understand how deep learning works under the hood.
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