Deep Learning for NLP Part 8

Deep Learning for NLP – Part 8, available at $34.99, has an average rating of 4.39, with 13 lectures, based on 9 reviews, and has 131 subscribers.

You will learn about Deep Learning for Natural Language Processing Graph Neural Networks Graph convolutions Graph pooling Applications of GNNs for NLP DL for NLP This course is ideal for individuals who are Beginners in deep learning or Python developers interested in data science concepts or Masters or PhD students who wish to learn deep learning concepts quickly or Deep learning engineers and developers It is particularly useful for Beginners in deep learning or Python developers interested in data science concepts or Masters or PhD students who wish to learn deep learning concepts quickly or Deep learning engineers and developers.

Enroll now: Deep Learning for NLP – Part 8

Summary

Title: Deep Learning for NLP – Part 8

Price: $34.99

Average Rating: 4.39

Number of Lectures: 13

Number of Published Lectures: 13

Number of Curriculum Items: 13

Number of Published Curriculum Objects: 13

Original Price: ?1,199

Quality Status: approved

Status: Live

What You Will Learn

Deep Learning for Natural Language Processing

Graph Neural Networks

Graph convolutions

Graph pooling

Applications of GNNs for NLP

DL for NLP

Who Should Attend

Beginners in deep learning

Python developers interested in data science concepts

Masters or PhD students who wish to learn deep learning concepts quickly

Deep learning engineers and developers

Target Audiences

Beginners in deep learning

Python developers interested in data science concepts

Masters or PhD students who wish to learn deep learning concepts quickly

Deep learning engineers and developers

More and more evidence has demonstrated that graph representation learning especially graph neural networks (GNNs) has tremendously facilitated computational tasks on graphs including both node-focused and graph-focused tasks. The revolutionary advances brought by GNNs have also immensely contributed to the depth and breadth of the adoption of graph representation learning in real-world applications. For the classical application domains of graph representation learning such as recommender systems and social network analysis, GNNs result in state-of-the-art performance and bring them into new frontiers. Meanwhile, new application domains of GNNs have been continuously emerging such as combinational optimization, physics, and healthcare. These wide applications of GNNs enable diverse contributions and perspectives from disparate disciplines and make this research field truly interdisciplinary.

In this course, I will start by talking about basic graph data representation and concepts like node data, edge types, adjacency matrix and Laplacian matrix etc. Next, we will talk about broad kinds of graph learning tasks and discuss basic operations needed in a GNN: filtering and pooling. Further, we will discuss details of different types of graph filtering (i.e., neighborhood aggregation) methods. These include graph convolutional networks, graph attention networks, confidence GCNs, Syntactic GCNs and the general message passing neural network framework. Next, we will talk about three main types of graph pooling methods: Topology based pooling, Global pooling and Hierarchical pooling. Within each of these three types of graph pooling methods, we will discuss popular methods. For example, in topology pooling we will talk about Normalized Cut and Graclus mainly. In Global pooling, we will talk about Set2Set and SortPool. In Hierarchical pooling, we will talk about diffPool, gPool and SAGPool. Next, we will talk about three unsupervised graph neural network architectures: GraphSAGE, Graph auto-encoders and Deep Graph InfoMax. Lastly, we will talk about some applications of GNNs for NLP including semantic role labeling, event detection, multiple event extraction, neural machine translation, document timestamping and relation extraction.

Course Curriculum

Chapter 1: Graph Neural Networks

Lecture 1: Introduction

Lecture 2: Graph Data

Lecture 3: Tasks on Graph-Structured Data

Lecture 4: Graph Filtering: Neighborhood aggregation schemes

Lecture 5: Graph Pooling (downsampling) Introduction

Lecture 6: Graph Pooling: Topology based pooling

Lecture 7: Graph Pooling: Global pooling

Lecture 8: Hierarchical Graph Pooling: Differentiable Pooling (DiffPool)

Lecture 9: Hierarchical Graph Pooling: gPool

Lecture 10: Hierarchical Graph Pooling: SAGPool

Lecture 11: Unsupervised Learning using GNNs

Lecture 12: Some applications of Graph Neural Nets

Lecture 13: Summary

Instructors

Manish Gupta
Principal Applied Researcher

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 0 votes

4 stars: 3 votes

5 stars: 5 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!