Titlebook: Information-Driven Machine Learning; Data Science as an E Gerald Friedland Textbook 2024 The Editor(s) (if applicable) and The Author(s), u

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prohibitive length. As a woman of colour, Cardi’s manicure can signal a certain economic arrival; her artificial nails are an indisputable signifier of disposable income used for personal services. Cardi B’s manicure counters historical narratives of subordination that situate women of colour at th

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Introduction, describing a profession on the job market. The term “data scientist” had been used in various capacities prior to this, but this article mainly contributed to the role gaining mainstream recognition as a distinct profession. It has since become a highly sought-after job role in many industries due

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The Automated Scientific Process,rgone a significant transformation in the recent past, particularly due to the advent of computers. Furthermore, we aim to provide a mathematical formulation for this change. This effort paves the way for understanding and applying the measurement and quantification concepts that are integral to thi

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Capacity, generalize the experimental results into a rule that can predict future experiments, most people would intuitively agree that a model should not be more complex than the training data. We introduced this intuition in Chap. ., especially Sect. .. More formally, the information content of the trainin

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Capacity of Neural Networks,hapter shows how to generalize the memory-equivalent capacity of a single neuron to a network of neurons. This question has sparked many approaches in the machine learning community, including fields such as “neural architecture search” or the wider field of “hyper parameter optimization (HPO).” How

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Capacities of Some Other Machine Learning Methods,in a machine learning model, and it brings us one step closer to reproducibility (see Corollary . and Chap. .). This chapter therefore discusses other machine learning approaches and how to calculate their memory-equivalent capacity (MEC).

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Measuring Data Sufficiency,accini (There Is No Data like More Data, . ‘The Voice in the Machine: Building Computers That Understand Speech’, The MIT Press, 2012)). Indeed, increasing the sample size is definitely a guaranteed way to increase the probability of coming up with a good model. However, data acquisition can be cost