In which we investigate convolutional neural networks, and train one with TensorFlow to distinguish cats from dogs.
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In which we discuss, code then visualise an autoencoder for learning the component visual features of handwritten digits.
Notes on probabilistic graphical models with reference to the Titanic disaster (the movie AND the boat)
In which we examine the strengths of probabilistic graphical models and build one to predict the survival of Titanic passengers.
A method for adaptive, supervised learning when you have streaming data.
In which we do a Bayesian optimal search of Italy using Roman scouts.
In which we use Bayesian regression to automatically adapt to the wartime shocks to Britain’s bread price.
In which we are archaeologists learning about how the Bayesian method mathematically encodes deductive reasoning.
In which we use Bayesian methods to figure the most likely breed of dog for Dogmatix.
In which Robot Fyodor Dostoyevsky says, "It's night I am in my room with a candle and suddenly there are devils all over the place in the paling where you can take a board out he gets through no one sees ."
In which we use Python to teach a computer the difference between three types of fiction.