Thoughtful, informed discussion of the future of AI and Machine Learning
Neural architecture search (NAS) is a popular area of machine learning, with the goal of automating the development of the best neural network for a given dataset. In this post, we summarize our recent paper, which suggests that existing NAS benchmarks may be too small to effectively evaluate NAS algorithms.
When reading about Machine Learning, the majority of the material you’ve encountered is likely concerned with classification problems. What about the other way around when you want to create data with predefined features? Today we’ll be breaking down VAEs and understanding the intuition behind them.
Pattern recognition is a crucial aspect of modern data analytics. These patterns can be studied to better understand the underlying structure of data and monitor behavior over time. However, there are often rare items or observations that seem to differ significantly from these patterns. These items are called anomalies (or outliers).
The painting “Edmond de Belamy” is an AI-generated portrait that was sold at an auction for $432,500 in 2018. That’s a lot of money for an artificial painting, but how exactly was it created? Generative Adversarial Networks.
Over just a few months, the new coronavirus disease (COVID-19) has grown from several cases in Wuhan province to a global health threat. News has been brimming with the term “flattening the curve”, which comes from what science currently understands about the spread of diseases like COVID-19.
Deep Learning (DL) has had immense success over the past few years in areas such as computer vision and speech recognition. Recently, DL techniques have also been used to enhance the performance of Recommender Systems (RS). Let’s dissect the underlying technologies and see why Deep Learning is the right choice for Recommender Systems.
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