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Deep Learning From Scratch II: Perceptrons

This is part 2 of a series of tutorials, in which we develop the mathematical and algorithmic underpinnings of deep neural networks from scratch and implement our own neural network library in Python,...

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Deep Learning From Scratch III: Training criterion

This is part 3 of a series of tutorials, in which we develop the mathematical and algorithmic underpinnings of deep neural networks from scratch and implement our own neural network library in Python,...

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Deep Learning From Scratch IV: Gradient Descent and Backpropagation

This is part 4 of a series of tutorials, in which we develop the mathematical and algorithmic underpinnings of deep neural networks from scratch and implement our own neural network library in Python,...

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Deep Learning From Scratch V: Multi-Layer Perceptrons

This is part 5 of a series of tutorials, in which we develop the mathematical and algorithmic underpinnings of deep neural networks from scratch and implement our own neural network library in Python,...

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Robot Localization I: Recursive Bayesian Estimation

This is part 1 in a series of tutorials in which we explore methods for robot localization: the problem of tracking the location of a robot over time with noisy sensors and noisy motors, which is an...

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Robot Localization II: The Histogram Filter

This is part 2 in a series of articles explaining methods for robot localization, i.e. determining and tracking a robot’s location via noisy sensor measurements. You should start with the first part:...

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Dealing with Unbalanced Classes in Machine Learning

In many real-world classification problems, we stumble upon training data with unbalanced classes. This means that the individual classes do not contain the same number of elements. For example, if we...

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Robot Localization IV: The Particle Filter

This is part 4 in a series of articles explaining methods for robot localization, i.e. determining and tracking a robot’s location via noisy sensor measurements. You should start with the first part:...

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Connectionist Models of Cognition

In this video, I give an introduction to the field of computational cognitive modeling (i.e. modeling minds through algorithms) in general, and connectionist modeling (i.e. using artificial neural...

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Deep Learning From Scratch VI: TensorFlow

This is part 6 of a series of tutorials, in which we develop the mathematical and algorithmic underpinnings of deep neural networks from scratch and implement our own neural network library in Python,...

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Building a Content-Based Search Engine I: Quantifying Similarity

This is part 1 in a series of tutorials in which we learn how to build a content-based search engine that retrieves multimedia objects based on their content rather than based on keywords, title or...

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Building a Content-Based Search Engine II: Extracting Feature Vectors

This is part 2 in a series of tutorials in which we learn how to build a content-based search engine that retrieves multimedia objects based on their content rather than based on keywords, title or...

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Building a Content-Based Search Engine III: Feature Signatures

This is part 3 in a series of tutorials in which we learn how to build a content-based search engine that retrieves multimedia objects based on their content rather than based on keywords, title or...

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Building a Content-Based Search Engine IV: Earth Mover’s Distance

This is part 4 in a series of tutorials in which we learn how to build a content-based search engine that retrieves multimedia objects based on their content rather than based on keywords, title or...

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Building a Content-Based Search Engine V: Signature Quadratic Form Distance

This is part 5 in a series of tutorials in which we learn how to build a content-based search engine that retrieves multimedia objects based on their content rather than based on keywords, title or...

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Building a Content-Based Search Engine VI: Efficient Query Processing

This is part 6 in a series of tutorials in which we learn how to build a content-based search engine that retrieves multimedia objects based on their content rather than based on keywords, title or...

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