Modern NLP: Data Collection, Annotation & Biases

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Language Models: Fixed-context and Recurrent Neural Networks

Discusses language models, focusing on fixed-context neural models and recurrent neural networks.

Transformers: Revolutionizing Attention Mechanisms in NLP

Covers the development of transformers and their impact on attention mechanisms in NLP.

Question Answering: Deep Learning Insights

Explores question answering systems, reading comprehension models, and the challenges in achieving accurate responses.

Neuro-symbolic Representations: Commonsense Knowledge & Reasoning

Delves into neuro-symbolic representations for commonsense knowledge and reasoning in natural language processing applications.

Pre-Training: BiLSTM and Transformer

Delves into pre-training BiLSTM and Transformer models for NLP tasks, showcasing their effectiveness and applications.

Words Tokens: Lexical Level Overview

Explores words, tokens, and language models in NLP, covering challenges in defining them, lexicon usage, n-grams, and probability estimation.

Data-Driven Insights: NLP and AI Applications

Explores building OS for heterogeneous hardware, data movement efficiency, AI advancements, and NLP challenges.

Deep Learning for NLP

Delves into Deep Learning for Natural Language Processing, exploring Neural Word Embeddings, Recurrent Neural Networks, and Attentive Neural Modeling with Transformers.

Transformer Architecture: Subquadratic Attention Mechanisms

Covers transformer architecture, focusing on encoder-decoder models and subquadratic attention mechanisms for efficient processing of input sequences.

Neural Networks for NLP

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.