by The global deep learning market size accounted for US$ 52.13 Bn in 2022 and is projected to reach around USD 978.88 Bn by 2032, growing at a CAGR of 34.08% from 2023 to 2032.
Key Takeaways:
- North America dominated the market with the highest market share of 37% in 2022.
- Asia Pacific is estimated to expand at the fastest CAGR during the forecast period.
- By type, the software segment is expected to sustain its dominance throughout the forecast period.
- By application, the image recognition segment is expected to witness significant growth during the forecast period.
- By end-user, the retail segment is expected to expand at a robust pace during the forecast period, the segment also held a significant share in 2022.
Report Summary
The global deep learning market report provides a Point-by-Point and In-Depth analysis of global market size, regional and country-level market size, market share, segmentation market growth, competitive landscape, sales analysis, opportunities analysis, strategic market growth analysis, the impact of domestic and global market key players, value chain optimization, trade regulations, recent developments, product launches, area marketplace expanding, and technological innovations.
The study offers a comprehensive analysis on diverse features, including production capacities, demand, product developments, revenue generation, and sales in the deep learning market across the globe.
A comprehensive estimate on the deep learning market has been provided through an optimistic scenario as well as a conservative scenario, taking into account the sales of deep learning during the forecast period. Price point comparison by region with global average price is also considered in the study.
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Deep Learning Market Report Scope
| Report Coverage | Details |
| Market Size in 2023 | USD 69.9 Billion |
| Market Size by 2032 | USD 978.88 Billion |
| Growth Rate from 2023 to 2032 | CAGR of 34.08% |
| Largest Market | North America |
| Base Year | 2022 |
| Forecast Period | 2023 to 2032 |
| Segments Covered | By Type, By Application, and By End-user |
| Regions Covered | North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa |
Market Segments:
Application:
Deep learning has found applications in stock photography and video websites, making visual content more accessible to users. This technology enables visual search, allowing users to find similar images or products using a reference image. Additionally, deep learning is utilized in various fields, such as medical image analysis, facial recognition for security and surveillance, and image detection in social media analytics.
The growth of visual content on social media platforms and the demand for content modernization are significant drivers for the deep learning market in image recognition applications. For example, in 2018, Instagram introduced a feature based on deep learning algorithms that describe photos to users with visual impairments. This feature identifies images using image recognition technology and automatically generates descriptions for them.
During the forecast period, data mining applications are projected to experience the highest Compound Annual Growth Rate (CAGR) of over 37%. Deep learning plays a vital role in addressing challenges in data mining and extraction processes, such as handling fast-moving streaming data, ensuring the reliability of data analysis, managing imbalanced input data, and dealing with highly distributed input sources. Deep learning algorithms excel in semantic indexing and tagging of videos, text, and images, performing discriminative tasks. They also demonstrate the ability to perform complex tasks through featured engineering, ultimately providing improved data representation.
End Users:
The autonomous vehicle represents a groundbreaking technology that demands immense computational power. Leveraging a Deep Neural Network (DNN), the autonomous vehicle can swiftly execute a wide array of tasks without human intervention.
Anticipated to gain significant momentum in the forthcoming years, the development of autonomous vehicles has garnered the attention of numerous startups and major corporations. Among them, Google Inc., Uber Technologies, Inc., and Tesla, Inc. stand out as prominent players, showcasing their prowess in this domain. Notably, in December 2019, Nvidia introduced the NVIDIA DRIVE platform dedicated to autonomous vehicles.
Substantial investments are being poured into advancing deep learning techniques, aimed at refining the capabilities of autonomous vehicles. A noteworthy example is Wayve, a London-based startup, which secured a substantial funding of USD 200 million in January 2022. This funding boost is poised to aid organizations in developing cutting-edge deep learning methodologies, enabling the training and evolution of artificial intelligence capable of handling complex driving scenarios.
Key Highlights:
Reports Coverage: It incorporates key market sections, key makers secured, the extent of items offered in the years considered, worldwide containerized deep learning market and study goals. Moreover, it contacts the division study gave in the report based on the sort of item and applications.
Market Outline: This area stresses the key investigations, market development rate, serious scene, market drivers, patterns, and issues notwithstanding the naturally visible pointers.
Market Production by Region: The report conveys information identified with import and fare, income, creation, and key players of every single local market contemplated are canvassed right now.
Also Read: Artificial Intelligence in Agriculture Market Size To Cross USD 11.13 Bn By 2032
Market Players
The report includes the profiles of key deep learning market companies along with their SWOT analysis and market strategies. In addition, the report focuses on leading industry players with information such as company profiles, components and services offered, financial information, key development in past five years.
Major companies operating in this area
- Facebook Inc.
- Google LLC
- Microsoft Corporation
- IBM Corporation
- Amazon Web Services Inc.
Deep Learning Market Segmentation
By Type
- Software
- Hardware
- Services
By Application
- Image Recognition
- Signal Recognition
- Data Processing
By End-user
- Retail
- BFSI
- Manufacturing
- Healthcare
- Automotive
- Telecom and Media
Regional Segmentation
- North America (U.S., Canada, Mexico)
- Europe (Germany, France, U.K., Italy, Spain, Rest of Europe)
- Asia-Pacific (China, Japan, India, Southeast Asia and Rest of APAC)
- Latin America (Brazil and Rest of Latin America)
- Middle East and Africa (GCC, North Africa, South Africa, Rest of MEA)
Research Methodology
Secondary Research
It involves company databases such as Hoover’s: This assists us to recognize financial information, the structure of the market participants and industry’s competitive landscape.
The secondary research sources referred in the process are as follows:
- Governmental bodies, and organizations creating economic policies
- National and international social welfare institutions
- Company websites, financial reports and SEC filings, broker and investor reports
- Related patent and regulatory databases
- Statistical databases and market reports
- Corporate Presentations, news, press release, and specification sheet of Manufacturers
Primary Research
Primary research includes face-to-face interviews, online surveys, and telephonic interviews.
- Means of primary research: Email interactions, telephonic discussions and Questionnaire-based research etc.
- In order to validate our research findings and analysis, we conduct primary interviews of key industry participants. Insights from primary respondents help in validating the secondary research findings. It also develops Research Team’s expertise and market understanding.
TABLE OF CONTENT
Chapter 1. Introduction
1.1. Research Objective
1.2. Scope of the Study
1.3. Definition
Chapter 2. Research Methodology (Premium Insights)
2.1. Research Approach
2.2. Data Sources
2.3. Assumptions & Limitations
Chapter 3. Executive Summary
3.1. Market Snapshot
Chapter 4. Market Variables and Scope
4.1. Introduction
4.2. Market Classification and Scope
4.3. Industry Value Chain Analysis
4.3.1. Raw Material Procurement Analysis
4.3.2. Sales and Distribution Channel Analysis
4.3.3. Downstream Buyer Analysis
Chapter 5. COVID 19 Impact on Deep Learning Market
5.1. COVID-19 Landscape: Deep Learning Industry Impact
5.2. COVID 19 – Impact Assessment for the Industry
5.3. COVID 19 Impact: Global Major Government Policy
5.4. Market Trends and Opportunities in the COVID-19 Landscape
Chapter 6. Market Dynamics Analysis and Trends
6.1. Market Dynamics
6.1.1. Market Drivers
6.1.2. Market Restraints
6.1.3. Market Opportunities
6.2. Porter’s Five Forces Analysis
6.2.1. Bargaining power of suppliers
6.2.2. Bargaining power of buyers
6.2.3. Threat of substitute
6.2.4. Threat of new entrants
6.2.5. Degree of competition
Chapter 7. Competitive Landscape
7.1.1. Company Market Share/Positioning Analysis
7.1.2. Key Strategies Adopted by Players
7.1.3. Vendor Landscape
7.1.3.1. List of Suppliers
7.1.3.2. List of Buyers
Chapter 8. Global Deep Learning Market, By Type
8.1. Deep Learning Market, by Type, 2023-2032
8.1.1 Software
8.1.1.1. Market Revenue and Forecast (2020-2032)
8.1.2. Hardware
8.1.2.1. Market Revenue and Forecast (2020-2032)
8.1.3. Services
8.1.3.1. Market Revenue and Forecast (2020-2032)
Chapter 9. Global Deep Learning Market, By Application
9.1. Deep Learning Market, by Application, 2023-2032
9.1.1. Image Recognition
9.1.1.1. Market Revenue and Forecast (2020-2032)
9.1.2. Signal Recognition
9.1.2.1. Market Revenue and Forecast (2020-2032)
9.1.3. Data Processing
9.1.3.1. Market Revenue and Forecast (2020-2032)
Chapter 10. Global Deep Learning Market, By End-user
10.1. Deep Learning Market, by End-user, 2023-2032
10.1.1. Retail
10.1.1.1. Market Revenue and Forecast (2020-2032)
10.1.2. BFSI
10.1.2.1. Market Revenue and Forecast (2020-2032)
10.1.3. Manufacturing
10.1.3.1. Market Revenue and Forecast (2020-2032)
10.1.4. Healthcare
10.1.4.1. Market Revenue and Forecast (2020-2032)
10.1.5. Automotive
10.1.5.1. Market Revenue and Forecast (2020-2032)
10.1.6. Telecom and Media
10.1.6.1. Market Revenue and Forecast (2020-2032)
Chapter 11. Global Deep Learning Market, Regional Estimates and Trend Forecast
11.1. North America
11.1.1. Market Revenue and Forecast, by Type (2020-2032)
11.1.2. Market Revenue and Forecast, by Application (2020-2032)
11.1.3. Market Revenue and Forecast, by End-user (2020-2032)
11.1.4. U.S.
11.1.4.1. Market Revenue and Forecast, by Type (2020-2032)
11.1.4.2. Market Revenue and Forecast, by Application (2020-2032)
11.1.4.3. Market Revenue and Forecast, by End-user (2020-2032)
11.1.5. Rest of North America
11.1.5.1. Market Revenue and Forecast, by Type (2020-2032)
11.1.5.2. Market Revenue and Forecast, by Application (2020-2032)
11.1.5.3. Market Revenue and Forecast, by End-user (2020-2032)
11.2. Europe
11.2.1. Market Revenue and Forecast, by Type (2020-2032)
11.2.2. Market Revenue and Forecast, by Application (2020-2032)
11.2.3. Market Revenue and Forecast, by End-user (2020-2032)
11.2.4. UK
11.2.4.1. Market Revenue and Forecast, by Type (2020-2032)
11.2.4.2. Market Revenue and Forecast, by Application (2020-2032)
11.2.4.3. Market Revenue and Forecast, by End-user (2020-2032)
11.2.5. Germany
11.2.5.1. Market Revenue and Forecast, by Type (2020-2032)
11.2.5.2. Market Revenue and Forecast, by Application (2020-2032)
11.2.5.3. Market Revenue and Forecast, by End-user (2020-2032)
11.2.6. France
11.2.6.1. Market Revenue and Forecast, by Type (2020-2032)
11.2.6.2. Market Revenue and Forecast, by Application (2020-2032)
11.2.6.3. Market Revenue and Forecast, by End-user (2020-2032)
11.2.7. Rest of Europe
11.2.7.1. Market Revenue and Forecast, by Type (2020-2032)
11.2.7.2. Market Revenue and Forecast, by Application (2020-2032)
11.2.7.3. Market Revenue and Forecast, by End-user (2020-2032)
11.3. APAC
11.3.1. Market Revenue and Forecast, by Type (2020-2032)
11.3.2. Market Revenue and Forecast, by Application (2020-2032)
11.3.3. Market Revenue and Forecast, by End-user (2020-2032)
11.3.4. India
11.3.4.1. Market Revenue and Forecast, by Type (2020-2032)
11.3.4.2. Market Revenue and Forecast, by Application (2020-2032)
11.3.4.3. Market Revenue and Forecast, by End-user (2020-2032)
11.3.5. China
11.3.5.1. Market Revenue and Forecast, by Type (2020-2032)
11.3.5.2. Market Revenue and Forecast, by Application (2020-2032)
11.3.5.3. Market Revenue and Forecast, by End-user (2020-2032)
11.3.6. Japan
11.3.6.1. Market Revenue and Forecast, by Type (2020-2032)
11.3.6.2. Market Revenue and Forecast, by Application (2020-2032)
11.3.6.3. Market Revenue and Forecast, by End-user (2020-2032)
11.3.7. Rest of APAC
11.3.7.1. Market Revenue and Forecast, by Type (2020-2032)
11.3.7.2. Market Revenue and Forecast, by Application (2020-2032)
11.3.7.3. Market Revenue and Forecast, by End-user (2020-2032)
11.4. MEA
11.4.1. Market Revenue and Forecast, by Type (2020-2032)
11.4.2. Market Revenue and Forecast, by Application (2020-2032)
11.4.3. Market Revenue and Forecast, by End-user (2020-2032)
11.4.4. GCC
11.4.4.1. Market Revenue and Forecast, by Type (2020-2032)
11.4.4.2. Market Revenue and Forecast, by Application (2020-2032)
11.4.4.3. Market Revenue and Forecast, by End-user (2020-2032)
11.4.5. North Africa
11.4.5.1. Market Revenue and Forecast, by Type (2020-2032)
11.4.5.2. Market Revenue and Forecast, by Application (2020-2032)
11.4.5.3. Market Revenue and Forecast, by End-user (2020-2032)
11.4.6. South Africa
11.4.6.1. Market Revenue and Forecast, by Type (2020-2032)
11.4.6.2. Market Revenue and Forecast, by Application (2020-2032)
11.4.6.3. Market Revenue and Forecast, by End-user (2020-2032)
11.4.7. Rest of MEA
11.4.7.1. Market Revenue and Forecast, by Type (2020-2032)
11.4.7.2. Market Revenue and Forecast, by Application (2020-2032)
11.4.7.3. Market Revenue and Forecast, by End-user (2020-2032)
11.5. Latin America
11.5.1. Market Revenue and Forecast, by Type (2020-2032)
11.5.2. Market Revenue and Forecast, by Application (2020-2032)
11.5.3. Market Revenue and Forecast, by End-user (2020-2032)
11.5.4. Brazil
11.5.4.1. Market Revenue and Forecast, by Type (2020-2032)
11.5.4.2. Market Revenue and Forecast, by Application (2020-2032)
11.5.4.3. Market Revenue and Forecast, by End-user (2020-2032)
11.5.5. Rest of LATAM
11.5.5.1. Market Revenue and Forecast, by Type (2020-2032)
11.5.5.2. Market Revenue and Forecast, by Application (2020-2032)
11.5.5.3. Market Revenue and Forecast, by End-user (2020-2032)
Chapter 12. Company Profiles
12.1. Facebook Inc.
12.1.1. Company Overview
12.1.2. Product Offerings
12.1.3. Financial Performance
12.1.4. Recent Initiatives
12.2. Google LLC
12.2.1. Company Overview
12.2.2. Product Offerings
12.2.3. Financial Performance
12.2.4. Recent Initiatives
12.3. Microsoft Corporation
12.3.1. Company Overview
12.3.2. Product Offerings
12.3.3. Financial Performance
12.3.4. Recent Initiatives
12.4. IBM Corporation
12.4.1. Company Overview
12.4.2. Product Offerings
12.4.3. Financial Performance
12.4.4. Recent Initiatives
12.5. Amazon Web Services Inc.
12.5.1. Company Overview
12.5.2. Product Offerings
12.5.3. Financial Performance
12.5.4. Recent Initiatives
Chapter 13. Research Methodology
13.1. Primary Research
13.2. Secondary Research
13.3. Assumptions
Chapter 14. Appendix
14.1. About Us
14.2. Glossary of Terms
Thanks for reading you can also get individual chapter-wise sections or region-wise report versions such as North America, Europe, or the Asia Pacific.
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