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Methods for training models on time-series or ordered data sequences.
Distinguishing note: Focuses on temporal dependency capture in sequential data.
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This project is an educational toolkit that provides implementations of fundamental machine learning algorithms built from scratch. By avoiding high-level library abstractions, it serves as a pedagogical reference for understanding the mathematical foundations and core mechanics of supervised learning, unsupervised learning, and reinforcement learning models. The repository distinguishes itself through a modular approach to model construction, allowing users to build custom neural networks by chaining independent functional blocks. It covers a wide range of techniques, including gradient-base
Processes time-series data by using recurrent neural networks to capture temporal dependencies.