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Why Machines Learn: A Book Review and Summary
- Anil Ananthaswamy combining his background in electronics and computer engineering with his talent for making complex ideas accessible, wrote Why Machines Learn to explore how machine learning has evolved.
- It covers the mathematical and computational underpinnings of AI and its societal implications.
- The book begins with the foundational concept of machine learning: identifying and learning from patterns in data.
- Ananthaswamy introduces the concept of the perceptron—the first artificial neural network.
- The book illustrates how Bayes’s Theorem provides a systematic way to incorporate new data into existing models, laying the groundwork for probabilistic reasoning in machine learning.
- It introduces Voronoi diagrams, a method for dividing space into regions based on proximity to a set of points, and connects them to the workings of the k-nearest neighbor (k-NN) algorithm.
- This chapter introduces dimensionality reduction, a technique used to simplify datasets by reducing the number of features.
- This chapter explores innovations that emerged well after my college years, introducing me to powerful tools like kernel methods and support vector machines (SVMs).
- The chapter also explores the concept of receptive fields, the portions of an image that each neuron in a CNN is sensitive to.
- The epilogue dedicates to examining the capabilities and limitations of large language models (LLMs).
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