ABSTRACT
Deep learning, a subfield of machine learning, has driven the fast advancement in artificial intelligence research, prompting astounding forward leaps on long-standing issues in a plenty of fields, for example, computer vision, natural language processing, and signal processing. Deep learning isn’t just a neural network with at least two hidden layers. The genuine substance of deep learning lies in the techniques that empower the expanded extraction of data obtained from a neural network with more than one hidden layer. Adding more hidden layers to a neural network would give little profit without the techniques that support the effective extraction of data.
This chapter proposes an exhaustive and in-depth analysis of the literature; how, where, and why deep learning models are applied in image processing, natural language processing, and signal processing. With recent progressions in deep learning-based computer vision models, applications are simpler to create than at any other time. This chapter investigates computer vision algorithms and deep convolutional neural networks (CNNs) to identify, track, and perceive continuously objects added with image segmentation. Natural language processing considers associations among computer and people utilizing common language. By and by, it is extremely regular to utilize common language preparing strategies to measure and dissect text. The portrayal of every token (word or subword) can be pretrained on an enormous corpus, utilizing word2vec, GloVe, or subword implanting models and text classification. Sound information investigation is tied in with breaking down and understanding sound signs caught by advanced gadgets, with various applications in different endeavors.
