Applied Machine Learning on Sensing by Atiqur Rahman Ahad (.ePUB)
File Size: 10 MB
Applied Machine Learning on Sensing Technologies by Atiqur Rahman Ahad, Anton Nijholt, Abdus Samad Kamal
Requirements: .ePUB reader, 10 MB | True EPUB
Overview: This book explores applied Machine Learning and Deep Learning in the field of sensing, vision and sensor-based applications. It includes a series of methodologies, exploration of new applications, presentations on relevant datasets, challenging applications, guidelines, ideas and future scopes. Edited by leading experts in these arenas, the book will be of great interest to academic researchers, graduate students and industry professionals in the fields of Machine Learning, Deep Learning, AI, sensing, computer vision and sensors. Traditional ML models are employed with the available information to recommend a crop for the selected land and provide decent accuracy. These models include unsupervised models like K-Means and other clustering algorithms as well as supervised models like Naive Bayes (NB), Support Vector Machines (SVM), and Neural Networks (NN). However, they require a significant number of manual interventions during the model design phases, such as data pretreatment, feature engineering, and so on. In addition, they are time-consuming and necessitate the participation of domain specialists. Deep Learning models differ from the previously described ML models in the way that they tune the model parameters using automatic feature learning techniques. The classical Deep Learning models, including Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), etc., can identify images but fail on sequential data such as text recognition, where the interrelationship of the sentences must be considered for output generation. In such cases, Recurrent NN (RNN) plays a vital role.
Genre: Non-Fiction > Tech & Devices
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