Deep Learning for NLP

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Related lectures (32)

Page 2 of 4

Pre-Training: BiLSTM and Transformer

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

Transformers: Revolutionizing Attention Mechanisms in NLP

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

Transformers: Pretraining and Decoding Techniques

Covers advanced transformer concepts, focusing on pretraining and decoding techniques in NLP.

Language Models: From Theory to Computation

Explores the mathematics of language models, covering architecture design, pre-training, and fine-tuning, emphasizing the importance of pre-training and fine-tuning for various tasks.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Foundations of Deep Learning: Transformer Architecture Overview

Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.

Long Short-Term Memory Networks

Introduces Long Short-Term Memory (LSTM) networks as a solution to vanishing and exploding gradients in recurrent neural networks.

Contextual Representations: ELMO and BERT Overview

Covers contextual representations in NLP, focusing on ELMO and BERT architectures and their applications in various tasks.

Language Modelling and Recurrent Neural Networks

Explores language modelling, RNNs, n-gram models, LSTMs, and bidirectional RNNs.

Non conceptual knowledge systems

Explores the impact of Deep learning on Digital Humanities, focusing on non conceptual knowledge systems and recent advancements in AI.