Audio processing is one of the most important application domains of digital signal processing (DSP) and machine learning. Modeling acoustic environments is an essential step in developing digital audio processing systems such as: speech recognition, speech enhancement, acoustic echo cancellation, etc. Acoustic environments are filled with background noise that can have multiple sources. For example, […]
The post The Method of Moments Estimator for Gaussian Mixture Models appeared first on Towards Data Science.

While building my own LLM-based application, I found many prompt engineering guides, but few equivalent guides for determining the temperature setting. Of course, temperature is a simple numerical value while prompts can get mindblowingly complex, so it may feel trivial as a product decision. Still, choosing the right temperature can dramatically change the nature of […]
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Microsoft PowerBI is a one of the most popular business intelligence (BI) tools, and while it has all the features you need to create dynamic analytic reporting for stakeholders across the business, creating some advanced data visualizations is more challenging. This article will walk through how to create large network graph visualizations in Microsoft PowerBI […]
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Metric collection is an essential part of every machine learning project, enabling us to track model performance and monitor training progress. Ideally, metrics should be collected and computed without introducing any additional overhead to the training process. However, just like other components of the training loop, inefficient metric computation can introduce unnecessary overhead, increase training-step […]
The post Efficient Metric Collection in PyTorch: Avoiding the Performance Pitfalls of TorchMetrics appeared first on Towards Data Science.

Minimum cost flow optimization minimizes the cost of moving flow through a network of nodes and edges. Nodes include sources (supply) and sinks (demand), with different costs and capacity limits. The aim is to find the least costly way to move volume from sources to sinks while adhering to all capacity limitations. Applications Applications of […]
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This article is aimed at those who want to understand exactly how diffusion models work, with no prior knowledge expected. I’ve tried to use illustrations wherever possible to provide visual intuitions on each part of these models. I’ve kept mathematical notation and equations to a minimum, and where they are necessary I’ve tried to define […]
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Introduction “Money can’t buy happiness.” “You can’t judge a book by its cover.” “An apple a day keeps the doctor away.” You’ve probably heard these sayings several times, but do they actually hold up when we look at the data? In this article series, I want to take popular myths/sayings and put them to the […]
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DeepSeek has recently made quite a buzz in the AI community, thanks to its impressive performance at relatively low costs. I think this is a perfect opportunity to dive deeper into how Large Language Models (LLMs) are trained. In this article, we will focus on the Reinforcement Learning (RL) side of things: we will cover […]
The post Training Large Language Models: From TRPO to GRPO appeared first on Towards Data Science.