Requirements: .PDF reader, 11.3 MB
Overview: This book is designed to provide in-depth knowledge on how search plays a fundamental role in problem solving. Meant for undergraduate and graduate students pursuing courses in Computer Science and Artificial Intelligence, it covers a wide spectrum of search methods. Readers will be able to begin with simple approaches and gradually progress to more complex algorithms applied to a variety of problems. It demonstrates that search is all pervasive in Artificial Intelligence and equips the reader with the relevant skills. This book is meant for the serious practitioner-to-be of constructing intelligent machines. Machines that are aware of the world around them, that have goals to achieve, and the ability to imagine the future and make appropriate choices to achieve those goals. It is an introduction to a fundamental building block of Artificial Intelligence (AI). As the book shows, search is central to intelligence. A neuron is a simple device that computes a simple function of the inputs it receives. Collections of interconnected neurons can do complex computations. Insights into animal brains have prompted many researchers to pursue the path of creating artificial neural networks (ANNs). An ANN is a computational model that can be trained to perform certain tasks by repeatedly showing a stimulus and the expected response. Deep networks got further impetus with the availability of open source software like Tensorflow from Google that makes the task of implementing Machine Learning models easier for researchers. More recently, generative neural networks have been successfully deployed for language generation and even creating paintings, for example, from OpenAI. Generative models embody a form of unsupervised learning from large amounts of data, and are then trained to generate data like the one the algorithms were trained on.
Genre: Non-Fiction > Tech & Devices
Download Instructions:
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