Growing demand for deep learing through neural networks is driving the demand for the market.

SURREY, BRITISH COLUMBIA, CANADA, September 22, 2021 /EINPresswire.com/ -- 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.
The BFSI sector segment had a mentionable market share due to numerous application areas related to financial analysis, predictive costing, risk investigation, and others

By eliminating the logical burden from an application developer and disregarding the rule-based preset algorithms, deep neural networks sets an artificial humanlike cognizance which further opens up a wide range of new possibilities to solve many kind of applications without a human inspector.

Incorporating neural networks make the computer visions quite easier to work with and extends the limit of what the conventional programming could do
Growing demand for deep learing through neural networks is driving the demand for the market.

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Key players in the market include Google, Oracle, Microsoft, IBM, Qualcomm, Intel, Ward Systems, Starmind, Neurala, NeuralWare, and Clarifai, among others.

Deep Neural Networks Market Size – USD 1.26 Billion in 2019, Market Growth - CAGR of 21.4%, Market Trends – High demand in cognitive analytical insights
The market is projected to see a substantial growth owing to its huge implementation in various sectors especially in North American region. Increasing use of advanced technology in BFSI, IT & Telecommunication and Healthcare sectors is anticipated to stimulate demand for the deep neural networks in the region.

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.

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

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.

Deployment Mode Outlook (Revenue: USD Billion; 2017-2027)
Cloud
On-Premises

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End-Use Verticals Outlook (Revenue: USD Billion; 2017-2027)
BFSI
IT & Telecommunication
Electronics & Semiconductors
Aerospace & Defense
Healthcare & Biotechnology
Energy & Utilities
Manufacturing Industries
Retail & E-Commerce
Others

Regional Overview:
The global Deep Neural Networks market has been categorized on the basis of key geographical regions into North America, Asia Pacific, Europe, Latin America, and Middle East & Africa. It evaluates the presence of the global Deep Neural Networks market in the major regions with regards to market share, market size, revenue contribution, sales network and distribution channel, and other key elements.

Furthermore, the years considered for the study are as follows:
Historical years – 2016-2018
Base year – 2019
Forecast period – 2020 to 2025
Table of Content
Chapter 1. Methodology & Sources
1.1. Market Definition
1.2. Research Scope
1.3. Methodology
1.4. Research Sources
1.4.1. Primary
1.4.2. Secondary
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
4.2.2.1. Stringent environmental regulations
4.2.2.2. Rising need to reduce bacterial or algal contamination in water systems
4.2.2.3. Increasing demand for biocides for municipal water treatment
4.2.3. Market restraints analysis
4.2.3.1. Fluctuating prices of raw material
4.2.3.2. Present challenging economic conditions due to the pandemic
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 Application Insights & Trends, Revenue (USD Million), Volume (Kilo Tons)
Chapter 6. Deep Neural Networks Market By Product type Insights & Trends Revenue (USD Million), Volume (Kilo Tons)
Chapter 7. Deep Neural Networks Market Regional Outlook
Chapter 8. Competitive Landscape
Continued…

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