A Step-by-Step Implementation Using the PyTorch Library TorchRec — Recommending the right product to customers at the right time is a prevalent challenge across industries. For instance, bankers are constantly looking to suggest highly relevant services to their existing or potential customers. Retailers strive to recommend appealing products that meet customer tastes. …

Practical Application to Rehospitalization — Survival models are great for predicting the time for an event to occur. …

Practical Application to Customer Churn Prediction — Predicting the probability of an event occurring is good, predicting the time remaining before an event occurs is even better! Take the example of customer churn. What if, instead of predicting the probability of a customer leaving the company in the next months, you could predict this probability at several…

When It Comes to Labeling Whole Graphs, Not Just Nodes — Many real-life situations can be modeled as graphs, but turning the relational structure of these graphs into valuable information that can help solve complex tasks is a real challenge. Take the example of drug discovery. In the early stages of drug development, scientists need to screen large libraries often composed…

When It Comes to Forecasting Connections Within a Network — As many real-world problems can naturally be modeled as a network of nodes and edges, Graphical Neural Networks (GNNs) provide a powerful approach to solve them. By leveraging this inherent structure, they can learn more efficiently and solve complex problems where standard machine learning algorithms fail. In this context, the…

When It Comes to Node Classification — Recently, Graph Neural Networks (GNNs) have received a lot of attention. From marketing to social science to biology, they have been widely promoted as the new way of learning “smartly” from data. …

Comparing MQ Forecasters & Transformers to ML to Find Out — The encoder-decoder framework is undoubtedly one of the most popular concepts in deep learning. Widely used to solve sophisticated tasks such as machine translation, image captioning, and text summarization, it has led to great breakthroughs. However, when it comes to time series forecasting, the encoder-decoder framework has generated less noise…

Using RNNs & DeepAR Models to Find Out — Time series forecasting use cases are certainly the most common time series use cases, as they can be found in all types of industries and in various contexts. …

Leveraging the Network Structure of the Use Case to Boost Predictive Performance — Cracking a data science problem might seem today as easy as child’s play with all the powerful machine learning algorithms that are available today. When correctly adjusted and trained, these algorithms are able to efficiently extract key features from the data, learn common patterns, and eventually provide accurate predictions.