Global Deep Learning Market by Solutions (Software and Hardware); By Applications (Image Recognition, Voice Recognition, Video Surveillance and Diagnostics, Data mining and Others); By End-users (Medical, Automotive, Retail, Finance, Industrial, Aerospace and Defense, Media and Advertising, Oil, Gas and Energy and Others); By Regions (North America, EMEA, Latin America and APAC); Drivers, Opportunities, Restraints, Trends, and Forecast to 2023
- February, 2018
- Domain: ICT - Social, Mobile, Analytics & Cloud (SMAC),digital technologies
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Deep learning can be considered as a subset of machine learning and consists of algorithms that allow a software to self-train to execute tasks such as image and speech recognition by exposing multi-layered neural networks to bulk data. It can have a profound impact on various industries such as finance, automotive, aerospace, telecommunication and information technology, oil and gas, industrial, defense, media and advertising, medical and others. The increasing research and development activities in this domain is expanding the end use areas for the technology. The factors that contribute to the high market share are parallelization, high computing power, swift improvements in information storage capacity in automotive and healthcare industries. A few major applications for deep learning systems are in autonomous cars, data analytics, cyber security and fraud detection. It has become imperative for both small and big organizations to analyse and extract meaningful information from visual content. Advanced technologies such as graphic processing units are highly accepted in scientific disciplines such as deep learning and data sciences. Valuable insights are extracted from bulk data by using deep learning neural networks to improve customer experience and generate innovative products. The development in artificial intelligence capabilities in natural language processing, computer vision areas and image and speech recognition are driving the growth for deep learning.
The use cases for deep learning is diverse ranging from detecting gene abnormalities predicting weather patterns to identifying fraudulent insurance claims, stock market analysis, robotics, drones, finance, agriculture. Deep learning systems have wide applications in the banking and financial sector. It helps bank employees expand their capabilities so that they can focus more on customer interactions rather than regular banking transactions. The deep learning software can offer solutions based on a client’s background and history and thus can provide evidence and context-based reasoning for every problem. Industries worldwide are generating enormous data which require high processing power and this data is being generated at an unprecedented rate and volume. This has created an enormous opportunity for deep learning powered applications. A plethora of start-ups are coming up with vertical specific solutions and global corporations are supporting these start-ups to innovate faster.
Market Analysis
According to Infoholic Research, the Global Deep Learning market is expected to grow at a CAGR of 49.93% during the forecast period 2017–2023. The market is driven by factors such as faster processor performance, large training data size, and sophisticated neural nets. The future potential of the market is promising owing to opportunities such as development in big data technologies, expanding end-user base and extensive R&D. The market growth is curbed by restraining factors such as implementation challenges, rigid business models, dearth of skilled data scientists, affordability of organizations and data security concerns and inaccessibility.
Segmentation by Solutions
The market has been segmented and analyzed by the following components: Software and Hardware.
Segmentation by End-Users
The market has been segmented and analyzed by the following end-users: Medical, Automotive, Retail, Finance, IT & Telecommunications, Industrial, Aerospace and Defence, Media and Advertising, Oil, Gas and Energy and Others.
Segmentation by Regions
The market has been segmented and analyzed by the following regions: North America, EMEA, Latin America, APAC and Latin America.
Segmentation by Applications
The market has been segmented and analyzed by the following applications: Image Recognition, Voice Recognition, Video Surveillance and Diagnostics, Data mining and Others.
Benefits
The study covers and analyses the “Global Deep Learning Market”. Bringing out the complete key insights of the industry, the report aims to provide an opportunity for players to understand the latest trends, current market scenario, government initiatives, and technologies related to the market. In addition, it helps the venture capitalists in understanding the companies better and take informed decisions.
- The report covers drivers, restraints, and opportunities (DRO) affecting the market growth during the forecast period (2017–2023).
- It also contains an analysis of vendor profiles, which include financial health, business units, key business priorities, SWOT, strategies, and views.
- The report covers competitive landscape, which includes M&A, joint ventures and collaborations, and competitor comparison analysis.
- In the vendor profile section, for the companies that are privately held, financial information and revenue of segments will be limited.
- Industry Outlook
1.1.1. Industry Overview
1.1.2. Industry Trends
1.1.3. PEST Analysis
- Report Outline
2.1.1. Report Scope
2.1.2. Report Summary
2.1.3. Research Methodology
2.1.4. Report Assumptions
- Market Snapshot
3.1.1. Total Addressable Market
3.1.2. Segmented Addressable Market
3.1.3. Related Markets
3.1.4. Machine Learning Market
3.1.5. Artificial Intelligence Market
- Market Outlook
4.1.1. Overview
4.1.2. Regulatory Bodies and Standards
4.1.3. Porter 5 (Five) Forces
- Market Characteristics
5.1.1. Neural Network diagram
5.1.2. Use Cases of Deep Learning
5.1.3. Market Segmentation
5.1.4. Market Dynamics
5.1.5. Drivers
5.1.5.1. Faster Processor Performance
5.1.5.2. Large training data size
5.1.5.3. Sophisticated neural nets
5.1.6. Restraints
5.1.6.1. Implementation challenges
5.1.6.2. Rigid business models
5.1.6.3. Dearth of skilled data scientists
5.1.6.4. Affordability of organizations
5.1.6.5. Data security concerns and data inaccessibility
5.1.7. Opportunities
5.1.7.1. Development in Big Data Technologies
5.1.7.2. Expanding End-user Base
5.1.7.3. Extensive R&D
5.1.8. DRO – Impact Analysis
- Trends, Roadmap, and Projects
6.1.1. Market Trends & Impact
6.1.2. Technology Roadmap
- Geographic Segmentation: Market Size and Analysis
7.1.1. Overview
7.1.2. North America
7.1.2.1. US
7.1.2.2. Canada
7.1.3. EMEA
7.1.3.1. The UK
7.1.3.2. Germany
7.1.4. Asia Pacific
7.1.4.1. India
7.1.4.2. China
7.1.4.3. Japan
7.1.5. Latin America
7.2. Deep Learning Market by Solutions
7.2.1. Software Solutions
7.2.2. Hardware
7.3. Global Deep Learning Market by Applications
7.3.1. Image Recognition
7.3.2. Voice Recognition
7.3.3. Video Surveillance and Diagnostics
7.3.4. Data Mining
7.3.5. Others
7.4. Deep Learning Market by End-users
7.4.1. Medical
7.4.2. Automotive
7.4.3. Retail
7.4.4. Finance
7.4.5. IT & Telecommunication
7.4.6. Industrial
7.4.7. Aerospace and Defence
7.4.8. Media and Advertising
7.4.9. Oil, Gas and Energy
7.4.10. Others
- Vendors Profiles
8.1 Microsoft Corporation
8.1.1. Overview
8.1.2. Business Units
8.1.3. Microsoft Corporation in Deep Learning
8.1.4. Business Focus
8.1.5. SWOT Analysis
8.1.6. Business Strategies
8.2. IBM Corporation
8.2.1. Overview
8.2.2. Business Units
8.2.3. Geographic Revenue
8.2.4. IBM Corporation in Deep Learning
8.2.5. Business Focus
8.2.6. SWOT Analysis
8.2.7. Business Strategies
8.3. Amazon Web Services
8.3.1. Overview
8.3.2. Business Units
8.3.3. Geographic Revenue
8.3.4. Amazon Web Services in Deep Learning
8.3.5. Business Focus
8.3.6. SWOT Analysis
8.3.7. Business Strategies
8.4. Google Inc.
8.4.1. Overview
8.4.2. Business Units
8.4.3. Geographic Revenue
8.4.4. Google Inc. in Deep Learning
8.4.5. Business Focus
8.4.6. SWOT Analysis
8.4.7. Business Strategies
8.5. Nvidia Corporation
8.5.1. Overview
8.5.2. Business Units
8.5.3. Nvidia in Deep Learning
8.5.4. Business Focus
8.5.5. Business Strategies
8.5.6. Hewlett-Packard Development Company, L.P.
8.5.6.1. Overview
8.5.6.2. Business Segments
8.5.6.3. HP in Deep Learning
8.5.6.4. Business Focus
8.5.6.5. SWOT Analysis
8.5.6.6. Business Strategies
8.5.7. Baidu Inc.
8.5.7.1. Overview
8.5.7.2. Business Segments
8.5.7.3. Baidu Inc. in Deep Learning
8.5.7.4. Business Focus
8.5.7.5. SWOT Analysis
8.5.7.6. Business Strategies
8.5.8. Intel Corporation
8.5.8.1. Overview
8.5.8.2. Business Segments
8.5.8.3. Intel Corporation in Deep Learning
8.5.8.4. Business Focus
8.5.8.5. SWOT Analysis
8.5.8.6. Business Strategies
- Companies to Watch for
9.1.1. Deepmind Technologies Ltd. (Acquired by Google)
9.1.2. Overview
9.1.3. Deepmind Offerings
1.1. Deep Vision
9.1.4. Overview
9.1.5. Deep Vision Offerings
1.1. Bay Labs
Annexure
9.1.6. Abbreviations
TABLE 1 GLOBAL DEEP LEARNING MARKET REVENUE BY REGIONS, 2017-2023 ($MILLION) 32
TABLE 2 GLOBAL DEEP LEARNING MARKET REVENUE BY SOLUTIONS, 2017-2023 ($MILLION) 39
TABLE 3 GLOBAL DEEP LEARNING MARKET REVENUE BY APPLICATIONS, 2017-2023 ($MILLION) 42
TABLE 4 GLOBAL DEEP LEARNING MARKET REVENUE BY END-USERS, 2017-2023 ($MILLION) 47
Research Framework
Infoholic Research works on a holistic 360° approach in order to deliver high quality, validated and reliable information in our market reports. The Market estimation and forecasting involves following steps:
- Data Collation (Primary & Secondary)
- In-house Estimation (Based on proprietary data bases and Models)
- Market Triangulation
- Forecasting
Market related information is congregated from both primary and secondary sources.
Primary sources
Involved participants from all global stakeholders such as Solution providers, service providers, Industry associations, thought leaders etc. across levels such as CXOs, VPs and managers. Plus, our in-house industry experts having decades of industry experience contribute their consulting and advisory services.
Secondary sources
Include public sources such as regulatory frameworks, government IT spending, government demographic indicators, industry association statistics, and company publications along with paid sources such as Factiva, OneSource, Bloomberg among others.
