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A historical trend in the cognitive sciences has been to understand the brain as nature’s way of implementing a computer, a view often termed the Classical Computational Theory of Mind (CCTM). Early roots of this idea can be found in the development of formal logics as a means for modeling laws of reason. A formal [...]

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Evolutionary psychology is an approach to understand human behavior that combines insights gained from evolutionary biology, the computational sciences and the study of ancestral living conditions. It has been put forward as an opposing view to what Tooby and Cosmides (1992) call the Standard Social Science Model (SSSM), which has dominated the social and behavioral [...]

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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 meta description. Part I: Quantifying Similarity Part II: Extracting Feature Vectors Part III: Feature Signatures Part IV: Earth Mover's Distance [...]

The post Building a Content-Based Multimedia Search Engine VI: Efficient Query Processing appeared first on sabinasz.net.

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 meta description. Part I: Quantifying Similarity Part II: Extracting Feature Vectors Part III: Feature Signatures Part IV: Earth Mover's Distance [...]

The post Building a Content-Based Multimedia Search Engine V: Signature Quadratic Form Distance appeared first on sabinasz.net.

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 meta description. Part I: Quantifying Similarity Part II: Extracting Feature Vectors Part III: Feature Signatures Part IV: Earth Mover's Distance [...]

The post Building a Content-Based Multimedia Search Engine IV: Earth Mover’s Distance appeared first on sabinasz.net.

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 meta description. Part I: Quantifying Similarity Part II: Extracting Feature Vectors Part III: Feature Signatures Part IV: Earth Mover's Distance [...]

The post Building a Content-Based Multimedia Search Engine III: Feature Signatures appeared first on sabinasz.net.

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 meta description. Part I: Quantifying Similarity Part II: Extracting Feature Vectors Part III: Feature Signatures Part IV: Earth Mover's Distance [...]

The post Building a Content-Based Multimedia Search Engine II: Extracting Feature Vectors appeared first on sabinasz.net.

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 meta description. Part I: Quantifying Similarity Part II: Extracting Feature Vectors Part III: Feature Signatures Part IV: Earth Mover's Distance [...]

The post Building a Content-Based Multimedia Search Engine I: Quantifying Similarity appeared first on sabinasz.net.

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, mimicing the TensorFlow API. Start with the first part: I: Computational Graphs. Part I: Computational Graphs Part II: Perceptrons Part III: Training criterion [...]

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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 networks for the modeling) in particular. We deal with the following topics:The purpose of computational cognitive modelingWhere connectionist models fit into the broader pictureHow connectionist models work internally [...]

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