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AiLearning-Theory-Applying is a comprehensive educational repository designed to help learners quickly understand artificial intelligence theory and apply it in practical machine learning and deep learning projects. The repository provides extensive tutorials covering mathematical foundations, machine learning algorithms, deep learning concepts, and modern large language model architectures. It includes well-commented notebooks, datasets, and implementation examples that allow learners to reproduce experiments and understand the inner workings of various algorithms. The project also introduces important concepts such as probability theory, linear algebra, regression models, clustering methods, and neural network architectures. Advanced sections explore modern AI topics including transformers, BERT-based natural language processing systems, and practical competition-style machine learning workflows.
Features
- Educational notebooks covering AI theory and practical machine learning implementations
- Tutorials explaining mathematical foundations such as probability and linear algebra
- Examples of deep learning architectures including CNNs, RNNs, and transformers
- Hands-on projects with datasets for model experimentation
- Practical guidance for machine learning competitions and real-world applications
- NLP modules demonstrating BERT and transformer-based models
Project Samples
