ABSTRACT
With the exponential growth of the information technology, nowadays tremendous amounts of data including images, audio, text and videos, up to millions or billions, are collected for training machine learning models. Deep neural networks (DNNs) are one of the widely used methods today. Large companies in the uses these methods to recommends buyers with products, filter junk email or text-based hate speeches, understand and translate major languages in real time, and so on. Inspired by the trend, our work is dedicated to developing and training a deep neural network to extract meaningful patterns from a set of labelled data i.e. making generalizations. We show that DNNs can learn feature representations that can be successfully applied in a wide spectrum of application domains. We show how DNNs are applied to classification problems – grading of fresh tomato fruits based on their physical qualities using supervised learning approach.
Lawan, F (2021). Deep Learning Methods For Filter Extraction. Afribary. Retrieved from https://track.afribary.com/works/deep-learning-methods-for-filter-extraction
Lawan, Falalu "Deep Learning Methods For Filter Extraction" Afribary. Afribary, 13 Apr. 2021, https://track.afribary.com/works/deep-learning-methods-for-filter-extraction. Accessed 24 Nov. 2024.
Lawan, Falalu . "Deep Learning Methods For Filter Extraction". Afribary, Afribary, 13 Apr. 2021. Web. 24 Nov. 2024. < https://track.afribary.com/works/deep-learning-methods-for-filter-extraction >.
Lawan, Falalu . "Deep Learning Methods For Filter Extraction" Afribary (2021). Accessed November 24, 2024. https://track.afribary.com/works/deep-learning-methods-for-filter-extraction