The Future: Computational Linguistics and Deep Learning

Related lectures (31)

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Introduces deep learning concepts for NLP, covering word embeddings, RNNs, and Transformers, emphasizing self-attention and multi-headed attention.

Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.

Delves into the impact of deep learning on non-conceptual knowledge systems and the advancements in transformers and generative adversarial networks.

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

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

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

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Explores data representations, histograms, neural networks, and deep learning concepts.

Examines systematicity, compositionality, neural network challenges, and unsupervised learning in NLP.

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