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Research Topics in Deep Learning Models and Algorithms

Research Topics in Deep Learning Models and Algorithms

   Deep learning models are extremely beneficial in collecting, handling, interpreting, and analyzing a vast amount of data efficiently. Deep learning models are widely used for extracting abstract features for complex problems and provide superior performance over traditional methods. Deep Learning Model is a subset of the Machine Learning model that incorporates neural networks in successive layers to learn from data iteratively. The working of deep learning model based on the human brain for processing the datasets and making efficient decision making. Models can be trained by using a large set of labeled data and neural networks that contain many layers. Deep learning models are deeper alterations of artificial neural networks (ANNs) with multiple layers, whether linear or non-linear.

   Deep learning methods are categorized into supervised, semi-supervised, and unsupervised learning. Recurrent Neural Networks, Convolutional Neural Network, Deep Neural Network, Deep Belief Network, Generative Adversarial Network, Radial Basis Function Networks, Restricted Boltzmann Machine, Long short term memory networks, Autoencoder and Self Organizing Maps are the most commonly used deep learning algorithms.

   In recent years, Deep learning models have gained tremendous success in a wide range of applications, particularly object detection, health care, medical research, Natural language processing, speech, and audio processing, Virtual assistants, driver-less vehicles, Aerospace and defense, transportation prediction, disaster management, face recognition, fraud detection, and predictive forecasting. Future research directions of deep learning models are automation in data annotation, data preparation for ensuring data quality, black box perception, hybrid modeling, and uncertainty handling, and Lightweight Deep Learning Modeling for Next-Generation Smart Devices and Applications.

   • Deep learning allows computational models of multiple processing layers to learn and represent data with multiple levels of abstraction and capture intricate structures of large-scale data.

   • Deep learning models can intelligently analyze the data on a large scale and enhance the intelligence and the capabilities of an application.

   • Deep-learning algorithms are representation-learning methods that learn the multiple levels of representation and features of data in hierarchical structures through supervised and unsupervised strategies for the prediction and classification tasks.

   • Deep learning powers many aspects of modern society and have resisted the best attempts of the artificial intelligence community for many years.

   • Due to their capability to learn abstract representations, Deep Learning algorithms offer a new way to extract abstract features automatically and well suitable for the integration of heterogeneous data with multiple modalities.

   • Deep learning has outperformed previous state-of-the-art techniques in several tasks and produces outstanding results in various applications. The main two achievements of deep learning are top accuracy combined with an automatic approach for feature extraction for complex problems.

   • Nevertheless, it has some issues such as Scalability of DL approaches, Domain adaptation when applying deep learning in big data analytics, Dealing with causality in learning, and energy-efficient techniques for devices, including mobile intelligence and FPGAs.