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Multimodal Information Retrieval
In today’s rapidly evolving digital landscape, the wealth of available information has expanded beyond the boundaries of traditional text-based...
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Outlook
While multimodal information retrieval has several exciting applications and a high potential for impact on important problems, there are several...
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Multimodal Content Generation
In this chapter, we will review the advances that are being made in this new field of multimodal content generation and also discuss several...
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Retrieval Augmented Modeling
Till this point in our book, we have discussed the fundamental principles of information retrieval, exploring its key elements, and various...
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Transformer-Driven Models for Language, Vision, and Multimodality
In this chapter, we will learn about the modeling and learning techniques that drive multimodal applications. We will focus specifically on the...
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Introduction
In this book, our emphasis is on multimodal information retrieval, specifically concentrating on text and image data. The traditional unimodal...
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Efficient Motor Learning Through Action-Perception Cycles in Deep Kinematic Inference
How does the brain adapt to slow changes in the body’s kinematic chain? And how can it perform complex operations that need tool use? Here, we...
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Dynamical Perception-Action Loop Formation with Developmental Embodiment for Hierarchical Active Inference
To adapt an autonomous system to a newly given cognitive goal, we propose a method to dynamically combine multiple perception-action loops. Focusing...
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Contextual Qualitative Deterministic Models for Self-learning Embodied Agents
This work presents an approach for embodied agents that have to learn models from the least amount of prior knowledge, solely based on knowing which...
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Introduction and General Concepts of Machine Learning and Data Science
As a first step toward understanding how and why different machine learning algorithms work, we will begin this chapter with a discussion and...
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Exploratory Data Analysis
Exploratory data analysis (EDA) is an important item in the toolbox of statistics that is often used at the beginning of any data analysis workflow....
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A First Approach to Machine Learning with Linear Regression
Linear regression is one of the most accessible machine learning methods which has strong roots in the field of statistics. Problems of interest...
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Combinatorics and Probabilities
Probability deals with predicting the likelihood of future events, while statistics involves the analysis of the frequency of past events. Without...
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Materials Science Datasets and Data Generation
In this chapter, we explain how to obtain high-quality datasets for statistical and machine learning experiments. In particular, we present our...
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Machine Learning Techniques
The success of Machine Learning benefits from the availability of various numerical techniques and specialized technical concepts that help to make...
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Projects
This is a special chapter dealing with security projects. We have arranged the projects in three parts. Part 1 consists of projects that can be done...
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Standardization and Security Criteria: Security Evaluation of Computer Products
Our growing dependence on technology and the corresponding skyrocketing security problems arising from it have all created a high demand for...
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Deep Learning-Based Solution for Intrusion Detection in the Internet of Things
Securing the Internet of Things-based environment is a top priority for consumers, businesses, and governments. There are billions of devices...
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Introduction to Data Science
Data science is a new interdisciplinary field that has attracted a lot of interest from the media in recent years, where it has been presented as a...
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Regression Analysis
In this chapter, we introduce regression analysis andRegression analysis some of its applications in data scienceData science. Regression is related...