CS224W: Machine Learning with Graphs
Complex data can be represented as a graph of relationships between objects. Such networks are a fundamental tool for modeling social, technological, and biological systems. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By means of studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools apt to reveal insights on a variety of networks.
Topics include: representation learning and Graph Neural Networks; algorithms for the World Wide Web; reasoning over Knowledge Graphs; influence maximization; disease outbreak detection, social network analysis.
Links:
- Direct
- Videos
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #graphs #graph #gnn #knowledgegraphs #socialnetworks
@data_science_weekly
Complex data can be represented as a graph of relationships between objects. Such networks are a fundamental tool for modeling social, technological, and biological systems. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By means of studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools apt to reveal insights on a variety of networks.
Topics include: representation learning and Graph Neural Networks; algorithms for the World Wide Web; reasoning over Knowledge Graphs; influence maximization; disease outbreak detection, social network analysis.
Links:
- Direct
- Videos
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #graphs #graph #gnn #knowledgegraphs #socialnetworks
@data_science_weekly
Exceptional Resources for Data Science Interview Preparation. Part 3: Specialized Machine Learning
In the previous article, I shared materials for preparing for the stage on Classical Machine Learning.
In this article, we will look at materials that can be used to prepare for the section on specialized machine learning.
Table of contents
- Resources
- Deep Learning
- Natural Language Processing
- Computer Vision
- Graph Neural Networks
- Reinforcement Learning
- Recommender Systems
- Time Series
- Big Data
- Let’s sum it up
- What’s next?
NB:
I'm the author of the article.
It was initially published in Russian (on habr.com), then I published it on medium.com. So, for Russian speakers I recommend to read Russian version, for English speakers I recommend to read English version and both will benefit from starring the repository, which will be maintained and updated when new resources become available.
Links:
- Medium (eng)
- Habr (rus)
Navigational hashtags: #armknowledgesharing #armarticles
General hashtags: #interview #interviewpreparation #machinelearning #ml #deeplearning #dl #nlp #cv #rl #gnn #recsys
@data_science_weekly
In the previous article, I shared materials for preparing for the stage on Classical Machine Learning.
In this article, we will look at materials that can be used to prepare for the section on specialized machine learning.
Table of contents
- Resources
- Deep Learning
- Natural Language Processing
- Computer Vision
- Graph Neural Networks
- Reinforcement Learning
- Recommender Systems
- Time Series
- Big Data
- Let’s sum it up
- What’s next?
NB:
I'm the author of the article.
It was initially published in Russian (on habr.com), then I published it on medium.com. So, for Russian speakers I recommend to read Russian version, for English speakers I recommend to read English version and both will benefit from starring the repository, which will be maintained and updated when new resources become available.
Links:
- Medium (eng)
- Habr (rus)
Navigational hashtags: #armknowledgesharing #armarticles
General hashtags: #interview #interviewpreparation #machinelearning #ml #deeplearning #dl #nlp #cv #rl #gnn #recsys
@data_science_weekly