Global Deep Learning Market to Reach US$ 113.0 Billion by 2028, Stimulated by Adoption of the Internet of Things (IoT)

According to the latest report by IMARC Group, titled "Deep Learning Market: Global Industry Trends, Share, Size, Growth, Opportunity and Forecast 2023-2028," the global deep learning market size reached US$ 17.2 Billion in 2022. Deep learning is concerned with creating and using algorithms that are modeled after the structure and operation of the human brain, particularly neural networks, to solve challenging problems and make sense of enormous volumes of data. It is a potent method that enables computers to automatically identify significant patterns and representations from unprocessed incoming data and learn from examples. It refers to the deep neural networks' numerous layers of linked nodes, which provide the models with the ability to learn hierarchical representations of the input data. These networks are composed of an input layer, one or more hidden layers, and an output layer, with numerous nodes or synthetic neurons included in each layer. Additionally, it also contains hierarchies and complex correlation data, which enables users to handle a variety of tasks, including speech recognition, natural language processing, and picture identification.

Global Deep Learning Market Trends:

A considerable increase in data generation majorly drives the global market. Coupled with the advent of the internet, social media, and connected devices across the globe, this is impelling the demand for deep learning as it helps in fulfilling the requirement of massive data and easily stores and processes it economically. Along with this, the widespread adoption of deep learning across numerous industries, including healthcare, finance, retail, manufacturing, and autonomous vehicles, is significantly supporting the market. For instance, in the healthcare sector, deep learning algorithms aid in analyzing medical images to detect diseases, assist in diagnosis, and predict patient outcomes. Additionally, the extensive utilization of deep learning across financial institutions for fraud detection, risk assessment, and algorithmic trading is further impacting the demand. In addition, the growing popularity of techniques developed for computer vision, such as image classification and object detection, is positively influencing the market. Apart from this, the development of voice assistants, language translation systems, and sentiment analysis tools is also contributing to the market. Furthermore, the availability of high-performance graphics processing units (GPUs) and specialized hardware accelerators to enable the training and deployment of large-scale deep neural networks is creating a positive market outlook. On account of the aforementioned factors, the market is anticipated to reach a value of US$ 113.0 Billion by 2028, exhibiting a growth rate (CAGR) of 38.2% during 2023-2028.

Market Summary:

• On the basis of the product type, the market has been divided into software, services, and hardware.
• Based on the application, the market has been segmented into image recognition, signal recognition, data mining, and others.
• On the basis of the end-use industry, the market has been divided into security, manufacturing, retail, automotive, healthcare, agriculture, and others.
• Based on the architecture, the market has been segmented into RNN, CNN, DBN, DSN, and GRU.
• Region-wise, the market has been segmented into North America (the United States and Canada); Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, and others); Europe (Germany, France, the United Kingdom, Italy, Spain, Russia, and others); Latin America (Brazil, Mexico, and others); and the Middle East and Africa.
• The competitive landscape of the market has been studied in the report with the detailed profiles of the key players. Some of these players include Amazon Web Services (AWS), Google Inc., IBM, Intel, Micron Technology, Microsoft Corporation, Nvidia, Qualcomm, Samsung Electronics, Sensory Inc., Pathmind, Inc., and Xilinx.

Report Scope:

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