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Related lectures (30)

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Pre-Training: BiLSTM and Transformer

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

Deep Learning: Graphs and Transformers Overview

Covers deep learning concepts, focusing on graphs, transformers, and their applications in multimodal data processing.

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Data Annotation: Collection and Biases in NLP

Addresses data collection, annotation processes, and biases in natural language processing.

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Efficient Methods in Natural Language Processing

Explores the efficiency of training large language models and the environmental impact of AI.

Machine Learning Fundamentals

Covers fundamental principles, opportunities, and challenges in machine learning.

Model Compression Techniques: Enhancing Neural Networks

Covers model compression techniques to enhance the efficiency of large language models in production settings.

Second-Order Model Compression

Explores second-order model compression for massive deep neural networks, showcasing compression techniques and their impact on model accuracy.

Decoding from Neural Models

Explores decoding from neural models in modern NLP, covering encoder-decoder models, decoding algorithms, issues with argmax decoding, and the impact of beam size.