Название: Explainable Machine Learning Models and ArchitecturesАвтор: Suman Lata Tripathi, Mufti Mahmud
Издательство: Wiley-Scrivener
Год: 2023
Страниц: 273
Язык: английский
Формат: pdf (true)
Размер: 61.5 MB
Machine Learning and Deep Learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated circuit (IC) area with embedded bulk memories that further lead to more IC area. Trade-offs between power consumption, delay and IC area are always a concern of designers and researchers. New hardware architectures and accelerators are needed to explore and experiment with efficient Machine Learning models. Many real-time applications like processing of biomedical data in healthcare, smart transportation, satellite image analysis, and IoT-enabled systems to have a lot of scope for improvements in terms of accuracy, speed, computational powers and overall power consumption.
This book deals with the efficient Machine Learning and Deep Learning models that support high-speed processors with reconfigurable architectures like graphic processing units (GPUs) and field programmable gate arrays (FPGAs), or any hybrid system. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must have for any library.
The creation of an intelligent system that works like a human is due to Artificial intelligence (AI). It can be broadly classified into four techniques: Machine Learning, machine vision, automation and Robotics and natural language processing (NLP). These domains can learn from data provided, identify the hidden pattern and make decisions with human intervention. There are three types of Machine Learning: supervised learning, unsupervised learning, and reinforcement learning. Thus, to reduce the risk factor while decision making, machine learning techniques are more beneficial. The benefit of machine learning is that it can do the work automatically, once it learns what to do. Therefore, in this work, we discuss the theory behind Machine Learning techniques and the tasks they perform such as classification, regression, clustering, etc. We also provide a review of the state of the art of several Machine Learning algorithms like Naive Bayes, random forest, K-Means, SVM, etc., in detail.
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