Lectures related to Neural Network Syntactic Parsing

Syntactic Parsing and Graph2Graph Transformers

Covers syntactic parsing, dependency grammar, and graph-based neural network parsing.

Sequence to Sequence Models: Overview and Applications

Covers sequence to sequence models, their architecture, applications, and the role of attention mechanisms in improving performance.

Recurrent Neural Networks: Training and Challenges

Discusses recurrent neural networks, their training challenges, and solutions like LSTMs and GRUs.

CYK Algorithm for Parsing General Grammars

Explores parsing general grammars using the CYK algorithm and Chomsky Normal Form.

Syntax Trees: Parse vs AST

Explains syntax trees, parse vs AST, while language syntax, AST classes, and grammar ambiguities.

Neural Networks: Two Layers Neural Network

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Neural Networks Optimization

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

Transformers: Pretraining and Decoding Techniques

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

Deep Learning: Recurrent Neural Networks

Explores Recurrent Neural Networks for behavioral data, covering Deep Knowledge Tracing, LSTM, GRU networks, hyperparameter tuning, and time series prediction tasks.

Grammars and Parsers: An Overview of Language Processing

Provides an overview of grammars and parsers in computer language processing.

Deep Learning: Convolutional Neural Networks

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

Transformers: Revolutionizing Attention Mechanisms in NLP

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

CYK Parsing Algorithm for General Grammars

Introduces the CYK parsing algorithm for context-free grammars, explaining its handling of ambiguity and importance in parsing general grammars.

Contextual Representations: ELMO and BERT Overview

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

Nonlinear Supervised Learning

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Parsing with Combinators

Explores parsing text into trees using parser combinators in Scala, covering filtering, transforming, sequencing, alternatives, recursion, spaces handling, lexing, monadic nature, and for-notation.

Language Modelling and Recurrent Neural Networks

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

The Hidden Convex Optimization Landscape of Deep Neural Networks

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.

Syntactic Parsing: Dependency Structure Log in to Mediaspace to watch this video

Covers syntactic structure, dependency parsing, and neural network transition-based parsing, highlighting the importance of dependency structure in linguistic analysis.