Transformers: Revolutionizing Attention Mechanisms in NLP

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Covers the pretraining of sequence-to-sequence models, focusing on BART and T5 architectures.

Covers the role of Transformers in unifying various machine learning fields.

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

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

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Explores deep generative models, including mixtures of multinomials, PCA, deep autoencoders, convolutional autoencoders, and GANs.

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

By Antoine Bosselut introduces Natural Language Processing and its challenges, advancements in neural models, and course goals.

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

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

Discusses the implementation and optimization of recurring networks using LSTM models in deep learning.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

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