The Global Deep Neural Networks Market is projected to reach USD 5.98 billion in 2027. The market is expected to be driven owing to extensive rise in the big data analytics, emergence of the deep learning through neural networks and cognitive analytical procedures in various verticals including IT & Telecommunication, BFSI, e-commerce, and healthcare, among others. The rising implementation of the deep neural networks in clinical diagnosis, image & signal analysis and interpretation, and drug & vaccine development, among others, are propelling the market growth broadly.
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Further key findings from the report suggest
- Software and applications are the most commonly used attributes that have been incorporating deep neural networks in use for research simulators, building visualization to monitor training process, simulate the behavior of the consumers using the apps and software, among others. Software and application sub-segment is growing at a CAGR of 22.6% throughout the forecast period.
- The deep neural networks are widely used in the field of visualization and visual analytics for the communicating information and discovering meaningful insights by using various visual encodings to transform the abstract data into useful representations.
- In 2018, Switzerland based leading AI Tech company, Starmind, announced an investment of USD 15 Million in its self-learning next generation designing and algorithms, based on the artificial neural network.
- Key players in the market include Google, Oracle, Microsoft, IBM, Qualcomm, Intel, Ward Systems, Starmind, Neurala, NeuralWare, and Clarifai, among others.
The report further sheds light on the broad geographical fragmentation of the global Deep Neural Networks market, as well as various market segments and sub-segments categorized into product types, applications, and end-users. The regional overview in the global market report comprises the market size, value, share, volume, and cost analysis related to each region.
For the purpose of this report, Emergen Research has segmented into the global Deep Neural Networks Market on the basis of Component, Application, Deployment Mode, End-Use Verticals, and region:
Component Outlook (Revenue: USD Billion; 2017-2027)
- Software & Application
Application Outlook (Revenue: USD Billion; 2017-2027)
- Data Preprocessing
- Analytical Tools
Deployment Mode Outlook (Revenue: USD Billion; 2017-2027)
End-Use Verticals Outlook (Revenue: USD Billion; 2017-2027)
- IT & Telecommunication
- Electronics & Semiconductors
- Aerospace & Defense
- Healthcare & Biotechnology
- Energy & Utilities
- Manufacturing Industries
- Retail & E-Commerce
- North America
- Latin America
- Asia Pacific
- Middle East & Africa
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Focal Points of the Global Deep Neural Networks Market Report:
- Market Coverage: This section of the report covers significant details on the key manufacturers, vital market segments, product innovation scope, and the forecast years. Additionally, it describes in detail the range of the product and the global Deep Neural Networksmarket segmentation based on product type and application spectrum.
- Executive Summary: In this chapter, the global market growth rate, competitive landscape, drivers and constraints, trends, limitations, and the key market segments have been discussed at length.
- Regional Analysis: The report offers meaningful insights into the import and export trends, production and consumption capacities, estimated revenue share, and key players of each region dominating the market
- Competitive Landscape:The report also discusses the course of development of each market player in this industry vertical during the forecast period. It further details on the firms, industries, organizations, vendors, and local manufacturers engaged in this industry. The leading products and services to gain global and regional market shares form the competitive landscape of the Deep Neural Networks industry.
- Manufacturers’ portfolios: This section includes detailed information regarding the product portfolio of each local and global manufacturer, their strengths and weaknesses, product profiles, production value and capacity, and other vital information.
Table of Content
Chapter 1. Methodology & Sources
1.1. Market Definition
1.2. Research Scope
1.4. Research Sources
1.4.3. Paid Sources
1.5. Market Estimation Technique
Chapter 2. Executive Summary
2.1. Summary Snapshot, 2019-2027
Chapter 3. Key Insights
Chapter 4. Deep Neural Networks Market Segmentation & Impact Analysis
4.1. Deep Neural Networks Market Material Segmentation Analysis
4.2. Industrial Outlook
4.2.1. Market indicators analysis
4.2.2. Market drivers analysis
220.127.116.11. Requirement higher analytical solutions, learning ability and processing power
18.104.22.168. Higher growth of big data and online proliferation
22.214.171.124. High demand in the predictive analysis
4.2.3. Market restraints analysis
126.96.36.199. Difficulties in network structure and higher R&D cost
4.3. Technological Insights
4.4. Regulatory Framework
4.5. Porter’s Five Forces Analysis
4.6. Competitive Metric Space Analysis
4.7. Price trend Analysis
4.8. Covid-19 Impact Analysis
Chapter 5. Deep Neural Networks Market By Component Insights & Trends, Revenue (USD Billion)
5.1. Component Dynamics & Market Share, 2019 & 2027
5.1.1. Software & Application
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