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UCL Centre for AI is partnering with DeepMind to deliver a Deep Learning Lecture Series.
In the past decade, convolutional neural networks have revolutionised computer vision. In this lecture, we will take a closer look at convolutional network architectures through several case studies, ranging from the early 90's to the current state of the art. We will review some of the building blocks that are in common use today, discuss the challenges of training deep models, and strategies for finding effective architectures, with a focus on image recognition.
Bio: Sander Dieleman is a Research Scientist at DeepMind in London, UK, where he he has worked on the development of AlphaGo and WaveNet. He was previously a PhD student at Ghent University, where he conducted research on feature learning and deep learning techniques for learning hierarchical representations of musical audio signals. During his PhD he also developed the deep learning library Lasagne and won solo and team gold medals respectively in Kaggle's "Galaxy Zoo" competition and the first National Data Science Bowl. In the summer of 2014, he interned at Spotify in New York, where he worked on implementing audio-based music recommendation using deep learning on an industrial scale.