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Data Science Talk: Computational Graphs

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Instituto Superior Técnico

Avenida Rovisco Pais 1

1049-001 Lisboa

Portugal

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Machine Learning frameworks like Tensorflow, Theano and PyTorch allow the development of new predictive models in a quick, intuitive and sustainable way. They also allow for (almost) "out of the box" parallelization of the model estimation phase, by both using single machine CPUs, GPUs as well as multiple machines. At first sight, these tools seem to work differently from one another. But, in reality, they are all based on the idea: differentiable Computational Graph (dCG).

In this talk, we will introduce the basic concepts of computational graphs, how they unify all models that deal with optimization of differentiable functions. You will understand how models like linear/logistic regression, k-means, support vector machines, neural networks can share the same parameter estimation machinery. By having this new perspective on machine learning, we hope you start to look at machine learning like a computer program (because, today, that is what these models are) instead of a black box with some knobs and sliders to move.

We assume you know the following topics: (partial) derivatives, the derivative of a composed function, descent, matrix multiplication. The code snippets we will present are written in Python.

Estimated Duration: 40-60 mins

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Instituto Superior Técnico

Avenida Rovisco Pais 1

1049-001 Lisboa

Portugal

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