NLP notes for ML/DL principles, examples, and model deployment
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This repository offers a comprehensive collection of learning notes and code examples for Natural Language Processing (NLP), targeting students and practitioners interested in machine learning and deep learning. It covers foundational concepts, traditional models, neural network implementations, and advanced pre-trained models like Transformer, BERT, and ALBERT, with practical applications and deployment strategies.
How It Works
The notes are structured thematically, progressing from traditional NLP techniques (rule-based, probabilistic models, search algorithms) to core machine learning and neural network principles (backpropagation, CNNs, RNNs). It then delves into deep learning frameworks (TensorFlow, PyTorch), detailing their APIs, data pipelines, and model-building approaches. A significant portion is dedicated to NLP-specific tasks, including text preprocessing, word embeddings, sequence labeling (HMM, CRF, BiLSTM-CRF), attention mechanisms, Transformer architectures, and various BERT-based models and applications.
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Limitations & Caveats
The repository is presented as learning notes, and the depth of coverage for each topic can vary. Some advanced models or deployment scenarios might require further research or specific configurations not fully detailed. The lack of explicit licensing information could be a concern for commercial adoption.
5 years ago
Inactive