A gentle introduction to graph neural networks

Neural networks have been adapted to leverage the structure and properties of graphs. We explore the components needed for building a graph neural network - and motivate the design choices behind them.

The machine & deep learning compendium

The Compendium includes around 500 topics, that contains various summaries, links, and articles that I have read on numerous topics that I found interesting or that I had needed to learn. It include the majority of modern machine learning algorithms, statistics, feature selection, and engineering techniques, deep-learning, NLP, audio, deep & classic vision, time-series, anomaly detection, graphs, experiment management, and much more. In addition to strategic topics such as data science management and team building, and essential topics such as product management, product design, and a technology stack from a DS POV.

Using deep learning to detect abusive sequences of member activity

The Anti-Abuse AI Team at LinkedIn creates, deploys, and maintains models that detect and prevent many types of abuse, including the creation of fake accounts, member profile scraping, automated spam, and account takeovers.

How data shapes the Uber rider app

Data is crucial for our products. Data analytics help us provide a frictionless experience to the people that use our services. It also enables our engineers, product managers, data analysts, and data scientists to make informed decisions. The impact of data analysis can be seen in every screen of our app: what is displayed on the home screen, the order in which products are shown, what relevant messages are shown to users, what is stopping users from taking rides or signing up, and so on.

Multiplying matrices without multiplying

Multiplying matrices is among the most fundamental and compute-intensive operations in machine learning. Consequently, there has been significant work on efficiently approximating matrix multiplies. We introduce a learning-based algorithm for this task that greatly outperforms existing methods. Experiments using hundreds of matrices from diverse domains show that it often runs 100× faster than exact matrix products and 10× faster than current approximate methods.