Deep learning is a trending research topic in machine learning, which utilizes multiple layers of data representation to perform nonlinear processing to understand the levels of data abstractions. Deep learning can generate high-level data representations from large volumes of structured and unstructured data. The efficiency of deep learning algorithms depends on the good representation of the input data to build powerful computational models. The explosive growth of data in recent times and the remarkable advancement of low-cost hardware technologies have led to the emergence of new deep learning models rapidly. Deep learning has delivered powerful methods that enable remarkable achievement in many research fields, leading to groundbreaking advancements in deep learning applications. Many deep learning techniques have demonstrated promising state-of-the-art results across many interdisciplinary applications.
Deep Learning Approaches:Deep learning is a universal learning approach that is not task-specific and capable of solving almost all sorts of problems in diverse application domains. Types of deep learning are categorized as deep supervised learning, deep semi-supervised learning, deep unsupervised learning, and deep reinforcement learning.
• Supervised deep learning creates great impact as such, most of the deep learning models, namely Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU) follow supervised technique, utilizes labeled data for training a model and iteratively modify the network parameters for a good approximation of the desired outcomes.
• Deep semi-supervised learning efficiently employs deep learning algorithms under consistency regularization, generative models, graph-based methods, and holistic approaches based on partially labeled datasets.
• Deep unsupervised learning is well in clustering and nonlinear dimensionality reduction without the presence of data labels and exploits Auto-Encoders (AE), Restricted Boltzmann Machines (RBM), and Generative Adversarial Networks (GAN) and RNN for unsupervised learning in many application domains.
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Deep reinforcement learning is a fast developing approach and is remarkably used in an unknown environment and complex real-world applications, for instance, intelligent transport systems, communication, networking, and robotics.
Deep Learning Algorithms: Deep learning has been expanding rapidly, with many new networks and new models emerging regularly. Many potential deep learning algorithms are playing significant roles in information processing, such as Recursive Neural Network (RvNN), RNN, GAN, CNN, AE, RBM, Deep Belief Network (DBN), Deep Boltzmann Machine (BBM), Graph Neural Network (GNN), Graph Convolutional Networks (GCN), Deep Stacking Network (DSN), LSTM, GRU Network, and model transfers have completely altered our perception of information processing.
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RvNN represents a hierarchical structure to make predictions and classification with compositional vectors. The successful application of RvNN is Natural language Processing (NLP) to handle different modalities such as natural images and natural language sentences.
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RNN is a popularly applied deep learning algorithm, especially in NLP and speech processing. The sequential information in the RNN network conveys useful knowledge in many applications, and Deep RNN was also developed to reduce the difficult learning in deep networks. LSTM and GRU are variants of RNN that emerged for short-memory problems, and comparatively, GRU is more efficient in its execution.
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GAN is a unique network architecture exploited to generate novel data and make more accurate predictions in many image and signal processing applications through its generative invention.
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CNN is the commonly utilized deep learning architecture, and its applicability includes image-processing, computer vision, and medical imaging with better accuracy and improved performance.
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AE is an unsupervised deep learning algorithm capable of high dimensional data operations and representation of a set of data through dimensionality reduction. Several progressive variants of AE are developed with stack layered representation to produce a deep learning network.
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DBNs are evolved to solve slow learning, poor parameter selection, and many training dataset requirements in neural networks. DBN and RBM are extensively applied for data encoding, news clustering, image segmentation, and cybersecurity.
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GNN and GCN are state-of-the-art models for deep learning on graphs and produce superior performance in various graph-related problems. GCN is the special type of GNN that uses convolutional aggregations in computer vision and NLP domains.
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DSN are evolved to solve complex classification with many deep individual networks and work superior to DBNs due to their suitable network architecture.
Applications of Deep Learning: The target deep learning approach is to resolve the sophisticated aspects of the input with multiple levels of representation and strong learning ability. The applicability of deep learning reaches tremendous success and covers significant breadth and depth of research, such works not only across the particular field but also over the broad range of multi-disciplinary fields. Nowadays, deep learning models impart state-of-the-art performance in various application domains, including image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others.
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Deep learning has recently demonstrated several high impactful attempts in achieving high accuracy in natural language processing tasks, and areas of such tasks include sentiment analysis, machine translation, paraphrase identification, summarization, and question answering.
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In visual data processing, deep learning methods attain successful outcomes on various tasks, including image classification, object detection and semantic segmentation, and video processing.
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For speech processing, most of the current deep learning research works focus on speech emotion recognition, speech enhancement, and speaker separation.
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Deep learning has shown promising performance in social network analysis, adopted for semantic evaluation, link prediction, and crisis response.
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Deep learning greatly impacts information retrieval, for instance, document retrieval and web search with the help of deep structured semantic modeling and a deep stacking network.
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Deep learning architectures are applied for transportation network congestion evolution, destination prediction, traffic signal control, demand prediction, traffic flow prediction, transportation mode, and combinatorial optimization in the intelligent transportation system with low computational resources.
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Currently, a huge number of companies that are engrossed in self-driving automotive technologies that utilize deep learning models for autonomous driving systems were categorized into robotics approaches and behavioral cloning approaches in order to facilitate high-level driving decisions.
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Deep learning is a highly progressive research field. It accomplishes more complicated bio-medicine tasks by discovering new knowledge and revealing things undetectable by human beings.
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The explosive use of deep learning in NLP is high by concentrating on several core linguistic processing issues and more applications of computational linguistics such as sentiment analysis, machine translation, and question answering.
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Deep learning in disaster information management is still in its early stages that need to focus on time-sensitive data and provide the most accurate assistance in a nearly real-time manner.
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The tendency of big data analytics requires new and sophisticated algorithms, which are accomplished by deep learning techniques by using hybrid learning and training mechanisms to process data in real-time with high accuracy, speed and efficiency.
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Deep learning is explored for various Internet of Things (IoT) scenarios including, Industrial Internet of Things, Internet of Vehicles, smart grid, smart home, and smart medical
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Deep learning on edge computing requires sustainable computational resources for combining end devices, edge servers, and the cloud across multiple edge devices with privacy, bandwidth efficiency, and scalability.
Research Challenges in Deep learning: Even though deep learning approaches are proving their finest and have been solving a variety of complicated applications with multiple layers and a high level of abstraction. There are still several issues that oblige to be contented in the future of deep learning due to either their challenging nature or lack of data availability for the general public.
Lack of innovation in model structure - There is no complete implementation of the depth of the advantages of deep learning technology, which needs to realize the development of a new depth of learning model for effective integration.
Update training methods - Many training methods focus on supervised training; there is no real sense to achieve complete unsupervised training combined with supervised training.
Deep learning has many parameters learning bottlenecks with learning rate, local optima, saddle points, vanishing, and exploding gradients.
Reduce training time - As the complexity of the problem is bigger, the amount of information processed is essential, which means that there is a demand for more and more training time for the deep learning model to improve the accuracy and the speed of data processing
Online learning - The current training in deep learning does not contribute to the realization of online learning, and it is necessary to enhance the online learning ability based on an innovative deep learning model.
Overcome adversarial sample - a big problem in the current deep learning is the adversarial sample; the long-term development of deep learning is needed to solve the problem of precision and avoid the potential security problem.
Data dimensionality issue - It is another landmark challenge faced by deep learning due to the critical information and overfitting problem in classification, especially in medical domain applications.
Insufficient data samples problem - Currently, deep learning models are requisite to focus on data sparsity, missing data, and messy data conducive to obtaining the approximated information through observations rather than training. However, data of some applications suffer from incompleteness, heterogeneity, and unlabeled data are challenging for deep learning is a relevant problem.
Future Scopes of Deep learning: The rapid utilization of deep learning algorithms in different fields shows its success and versatility, clearly accentuating the growth of deep learning and the tendency for future advancement and research. Some of the future aspects of deep learning are listed below:
• Deep neural networks with a sophisticated and non-static noisy framework and with multiple noises are need enhancement
• The improvement of feature diversity in deep learning models will raise the performance of deep networks.
• Compatible deep neural networks need to be introduced in the unsupervised learning online environment.
• Huge future direction relies on enhancement in deep reinforcement learning.
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The upcoming deep neural network should be designed by considering inferences, efficiency, and the accuracies and maintenance of a wide repository of data.
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For developing deep generative models, superior and advanced temporal modeling abilities will be instigated for the parametric speech recognition system.
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In the medical domain, automatically assess Electrocardiogram (ECG) with deep learning methods required to be improved.
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The deep learning model with fully autonomous driving is a succeeding opportunity in the technology of self-driving cars.
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Other emerging research trends in deep learning are acceleration and optimization, distributed deep learning in IoT and Cyber-Physical Systems (CPS), network management and control, and security.