Posts
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...
deepideas.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...
deepideas.net
Reviews
5.0
2 Reviews
Tell people what you think
Photos
Posts
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. In the previous section, we saw how similarity between multimedia objects can be
deepideas.net
The explosion of user-generated content on the internet during the last decades has left the world of querying multimedia data with unprecedented challenges. There is a demand for this data to be processed and indexed in order to make it available for different types of queries, whilst ensuring acce...
deepideas.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: Computatio...
deepideas.net
In this video, I give an introduction to the field of computational cognitive modeling in general, and connectionist modeling in particular. We deal with the following topics: The purpose of computational cognitive modeling Where connectionist models fit into the broader picture How connectionist mo...
deepideas.net
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: Robot Localization I: Recursive Bayesian Estimation The last filtering algorithm we are going to dis...
deepideas.net
This is part 3 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: Robot Localization I: Recursive Bayesian Estimation This post deals with another solution to the con...
deepideas.net
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 want to build an image-based skin cancer detection system using convolutional neural networks, we m...
deepideas.net
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: Robot Localization I: Recursive Bayesian Estimation Idea The Histogram Filter is the most straightfo...
deepideas.net
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 important task for every autonomous robot, including self-driving cars. The methods that we will lear...
deepideas.net
This text deals with arguments against the possibility of so-called strong artificial intelligence, with a particular focus on the Chinese Room Argument devised by philosopher John Searle. We start…
deepideas.net
Deep Ideas updated their cover photo.
Image may contain: plant, night and outdoor
Introduction This text gives an overview of Gödel’s Incompleteness Theorem and its implications for artificial intelligence. Specifically, we deal with the question whether Gödel’s Incompleteness T…
deepideas.net
In this text, we develop the mathematical and algorithmic underpinnings of deep neural networks from scratch and implement our own neural network library in Python, mimicking the TensorFlow API. I …
deepideas.net