ML Algorithms Engineering Applications by Prasenjit Chatterjee (.PDF)+
File Size: 10 MB
Machine Learning Algorithms for Engineering Applications: Future Trends and Research Directions by Prasenjit Chatterjee, Parmanand Astya, Sudeshna Chakraborty and Pooja
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Overview: Machine Learning is a vital part of numerous academic and financial applications, in areas ranging from health care and treatment to finding relevant information in social networks. Large organisations thoughtfully apply Machine Learning algorithms with extensive research teams. The purpose of this book is to provide an intellectual introduction to statistical or Machine Learning (ML) techniques for those that would not normally be exposed to such approaches during their typical required statistical exercise. Statistical analysis is an integral part of Machine Learning and can be described as a form of it, often even utilising well-known and familiar techniques, that has a different focus than traditional analytical practice in applied disciplines. The key notion is that flexible, automatic approaches are used to detect patterns within the data, with a primary focus on making predictions on future data. Although there will be references using the R statistical environment and explanations of a few basic methods, the emphasis here is on theories rather than applications and will be held as hypothetical as possible. In terms of prerequisite expertise, we would presume a simple understanding of regression analyses as they are usually described in applicable disciplines. In terms of programming, none is really needed to follow any of the material here, but knowledge of R, CUDA, Mahout, Pytorch, TensorFlow, and Colab will be useful if one wants to do the examples.
Genre: Non-Fiction > Tech & Devices
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