I use PyMC3 to solve the food delivery toy problem and explore some alternative priors.

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I was grabbing a burger at Shake Shack, Mall of the Emirates in Dubai, when I noticed this notebook on the counter. The staff is using it to track food deliveries and each service (Carriage, Talabat, UberEats, Deliveroo) has its own column with the order numbers. Let's assume this is the only page for the day, and ask ourselves: given this data, what is the probability that UberEats is the most popular food delivery service?.

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The Collatz conjecture is a conjecture in mathematics that concerns a sequence defined as follows: start with any positive integer n. Then each term is obtained from the previous term as follows: if the previous term is even, the next term is one half the previous term. If the previous term is odd, the next term is 3 times the previous term plus 1. The conjecture is that no matter what value of n, the sequence will always reach 1.

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It’s easy to build a CNN that does well on MNIST digit classification. How easy is it to break it, to distort the images and cause the model to misclassify?

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CIFAR-10 is a classic image recognition problem, consisting of 60,000 32x32 pixel RGB images (50,000 for training and 10,000 for testing) in 10 categories: plane, car, bird, cat, deer, dog, frog, horse, ship, truck. Convolutional Neural Networks (CNN) do really well on CIFAR-10, achieving 99%+ accuracy. The Pytorch distribution includes an example CNN for solving CIFAR-10, at 45% accuracy. I will use that and merge it with a Tensorflow example implementation to achieve 75%. We use torchvision to avoid downloading and data wrangling the datasets. Like in the MNIST example, I use Scikit-Learn to calculate goodness metrics and plots.

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MNIST is a classic image recognition problem, specifically digit recognition. It contains 70,000 28x28 pixel grayscale images of hand-written, labeled images, 60,000 for training and 10,000 for testing. Convolutional Neural Networks (CNN) do really well on MNIST, achieving 99%+ accuracy. The Pytorch distribution includes a 4-layer CNN for solving MNIST. Here I will unpack and go through this example. We use torchvision to avoid downloading and data wrangling the datasets. Finally, instead of calculating performance metrics of the model by hand, I will extract results in a format so we can use SciKit-Learn's rich library of metrics.

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rxe is a thin wrapper around Python's re module. The various rxe functions are wrappers around corresponding re patterns. For example, rxe.digit().one_or_more('a').whitespace() corresponds to \da+\s. Because rxe uses parentheses but wants to avoid unnamed groups, the internal (equivalent) representation is actually \d(?:a)+\s. This pattern can always be retrieved with get_pattern().

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