Jan 7-11, 2019. 11:00am-12:30pm. Room: 2-135.
Description: The course will consist of five hands-on tutorials giving the students practical experience in programming, training, evaluating and benchmarking deep learning models in Julia. While other machine learning libraries can meet many needs, for innovators who want to go innovate beyond the ordinary models, the expressivity of Julia has no equal. After a brief introduction to the Julia programming language we will cover linear models, multi-layer perceptrons, convolutional and recurrent neural networks. Through these examples the students will be exposed to the concepts of optimization with stochastic gradient descent (backpropagation); data normalization and minibatching; overfitting and regularization; model architectures and sample efficiency.
Prerequisites: Familiarity with programming, probability, calculus and linear algebra.
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