Natural Language Processing
NLP from the ground up: word embeddings, attention mechanisms, transformers, text classification, and the building blocks of modern language understanding.
6 posts
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LLMRetrieval Augmented Generation (RAG) Chatbot for 10Q Financial Reports
Building a RAG-based chatbot for 10Q financial reports to reduce LLM hallucinations by grounding answers in retrieved document context.
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Deep LearningAttention
The attention mechanism in sequence-to-sequence models, how it allows the decoder to focus on relevant parts of the input at each step.
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Deep LearningBackpropagation Through Time
A mathematical deep dive into how gradients are computed in RNNs via Backpropagation Through Time (BPTT), explaining vanishing gradient origins.
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Deep LearningImage captioning using encoder-decoder
Building an image captioning system using a CNN encoder and RNN decoder based on the Show and Tell architecture.
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Deep LearningThe gradient problem in RNN
Why vanilla RNNs suffer from vanishing and exploding gradients, and how this limits their ability to capture long-range dependencies.
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Deep Learningword2vec: The foundation of NLP
How word2vec represents words as dense vectors by learning from context, solving the limitations of one-hot encoding for NLP tasks.