Machinery Desk

Deep Learning Market Insights, Forecast to 2019 Analysis by Application, Size, Production, Market Share, Consumption, Trends and Forecast 2026

Pressmeddelande   •   Mar 13, 2020 16:17 CET

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The deep learning market size was valued US$ 2.5 Bn in 2017 and is expected to reach US$ 21.1 Bn by 2026, at a CAGR of 30.58 % during forecast period 2019 to 2026. Deep learning is a part of machine learning which deals with algorithms similar to the functioning of the neural system in the brain. The language has three major forms of architecture, namely supervised, semi-supervised, and unsupervised. It has been applied in board games, drug design, bioinformatics, and material design.

Major factors that are contributing to the largest market share include parallelization, high computing power, and rapid improvements in fast information storage capacity in healthcare and automotive industries. Increase in need for the small and large enterprises to analyze and understand visual contents is projected to boost the growth of deep learning market. Advanced technology like graphics processing unit is highly accepted by the scientific disciplines like data science and deep learning. Deep learning neural networks are used in the associations to pull out valuable insights from extensive amounts of data to improve customer experience and provide innovative products, this may give enlargement to the market growth. This technology is having importance among the researchers and key players because of improving artificial intelligence capabilities in computer vision areas, natural language processing, and image & speech recognition. Asia Pacific is the most noticeable region for deep learning market and it is growing at a faster rate due to higher spending on cognitive computing technologies and artificial intelligence.

The report analyzes and forecasts the deep learning market at global and regional levels. The market has been forecast based on volume (Tons) and value (US$ Mn) from 2019 to 2026. The study includes drivers and restraints of the global market. It covers the impact of these drivers and restraints on the demand during the forecast period. The report also highlights opportunities in the market at the global level.

The report comprises a detailed value chain analysis, which provides a comprehensive view of the global deep learning market. The Porter’s Five Forces model has also been included to help understand the competitive landscape of the market. The study encompasses market attractiveness analysis, wherein various applications have been benchmarked based on their market size, growth rate, and general attractiveness.

The study provides a decisive view of the deep learning market by segmenting it in terms of form and application. The segment has been analyzed based on the present and future trends. Regional segmentation includes the current and projected demand in North America, Europe, Asia Pacific, Latin America, and Middle East & Africa.

The report provides size (in terms of volume and value) of deep learning market for the base year 2018 and the forecast between 2019 and 2026. Market numbers have been estimated based on form and application. Market size and forecast for each application segment have been provided for the global and regional market.

Scope of Report:

Global Deep Learning Market by Component:

  • Hardware
    • Software
  • Global Deep Learning Market by Application:

  • Speech Recognition
    • Image Recognition
    • Data Mining
    • Drug Discovery
    • Driver Assistance
    • Others
  • Global Deep Learning Market by Architecture Industry:

  • RNN
    • CNN
    • DBN
    • DSN
    • GRU
  • Global Deep Learning Market by End Use Industry:

  • Automotive
    • Healthcare
    • Media & Entertainment
    • BFSI
    • Other
  • Global Deep Learning Market by Geography:

  • North America
    • Asia Pacific
    • Europe
    • Latin America
    • Middle East and Africa
  • Key Players Operating Market Include:

  • Advanced Micro Devices, Inc.
    • Arm Ltd.
    • Baidu Inc.
    • Clairifai Inc.
    • Enlitic
    • General Vision Inc.
    • Google Inc.
    • Hewlett Packard
    • IBM Corporation
    • Intel Corporation
    • Microsoft Corporation
    • Nvidia Corporation
    • Qualcomm Technologies Inc.
    • Sensory Inc.
    • Skymind
    • Alphabet Inc.
    • Micron Technology Inc.
    • Amazon Web Services
  • Reasons to Buy the Report

    The report will enrich established firms as well as new entrants/smaller firms to gauge the pulse of the market, which, in turn, would help them garner a greater share of the market. Firms purchasing the report could use one or any combination of the below-mentioned strategies to strengthen their position in the market.

    This report provides insights into the following pointers:

    • Market Penetration: Comprehensive information on the product portfolios of the top players in the global deep learning market. The report analyzes this market by product, functionality, formulation, and region
    • Product Enhancement/Innovation: Detailed insights on the upcoming trends and product launches in the global deep learning market
    • Market Development: Comprehensive information on the lucrative emerging markets by product, functionality, formulation, and region
    • Market Diversification: Exhaustive information about new products or product enhancements, growing geographies, recent developments, and investments in the global deep learning market
    • Competitive Assessment: In-depth assessment of market shares, growth strategies, product offerings, and capabilities of leading players in the global deep learning market

    Research Methodology:

    In-depth interviews and discussions were conducted with several key market participants and opinion leaders to compile the research report.

    Primary research represents a bulk of research efforts, supplemented by extensive secondary research. Annual reports, press releases, and relevant documents of key players operating in various application areas have been reviewed for competition analysis and market understanding.

    Secondary research also includes recent trends, technical writing, Internet sources, and statistical data from government websites, trade associations, and agencies. These have proved to be reliable, effective, and successful approaches for obtaining precise market data, capturing market participants’ insights, and recognizing business opportunities.

    The study objectives of this report are:

    • To analyze and study the global deep learning capacity, production, value, consumption, status (2014-2018) and forecast (2019-2026);
    • Focuses on the key deep learning manufacturers, to study the capacity, production, value, market share and development plans in future.
    • Comprehensive company profiles covering the product offerings, key financial information, recent developments, SWOT analysis, and strategies employed by the major market players
    • To define, describe and forecast the market by type, application and region.
    • To analyze the global and key regions market potential and advantage, opportunity and challenge, restraints and risks.
    • To identify significant trends and factors driving or inhibiting the market growth.
    • To analyze the opportunities in the market for stakeholders by identifying the high growth segments.
    • To strategically analyze each submarket with respect to individual growth trend and their contribution to the market
    • To analyze competitive developments such as expansions, agreements, new product launches, and acquisitions in the market
    • To strategically profile the key players and comprehensively analyze their growth strategies.

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    1. Preface
    1.1. Report Scope and Market Segmentation
    1.2. Research Highlights
    1.3. Research Objectives

    2. Assumptions and Research Methodology
    2.1. Report Assumptions
    2.2. Abbreviations
    2.3. Research Methodology
    2.3.1. Secondary Research
    2.3.1.1. Secondary data
    2.3.1.2. Secondary Sources
    2.3.2. Primary Research
    2.3.2.1. Data from Primary Sources
    2.3.2.2. Breakdown of Primary Sources

    3. Executive Summary: Global Deep Learning Market, by Market Value (US$ Bn)
    4. Market Overview
    4.1. Introduction
    4.2. Market Indicator
    4.2.1. Drivers
    4.2.2. Restraints
    4.2.3. Opportunities
    4.2.4. Challenges
    4.3. Porter’s Analysis
    4.4. Value Chain Analysis
    4.5. Market Risk Analysis
    4.6. SWOT Analysis
    4.7. Global Deep Learning Market Industry Trends and Emerging Technologies

    5. Supply Side and Demand Side Indicators

    6. Global Deep Learning Market Analysis and Forecast
    6.1. Global Deep Learning Market Size & Y-o-Y Growth Analysis
    6.1.1. North America
    6.1.2. Europe
    6.1.3. Asia Pacific
    6.1.4. Middle East & Africa
    6.1.5. South America

    7. Global Deep Learning Market Analysis and Forecast, by Component
    7.1. Introduction and Definition
    7.2. Key Findings
    7.3. Global Deep Learning Market Value Share Analysis, by Component
    7.4. Global Deep Learning Market Size (US$ Bn) Forecast, by Component
    7.5. Global Deep Learning Market Analysis, by Component
    7.6. Global Deep Learning Market Attractiveness Analysis, by Component

    8. Global Deep Learning Market Analysis and Forecast, by Application
    8.1. Introduction and Definition
    8.2. Key Findings
    8.3. Global Deep Learning Market Value Share Analysis, by Application
    8.4. Global Deep Learning Market Size (US$ Bn) Forecast, by Application
    8.5. Global Deep Learning Market Analysis, by Application
    8.6. Global Deep Learning Market Attractiveness Analysis, by Application

    9. Global Deep Learning Market Analysis and Forecast, by Architecture Industry
    9.1. Introduction and Definition
    9.2. Key Findings
    9.3. Global Deep Learning Market Value Share Analysis, by Architecture Industry
    9.4. Global Deep Learning Market Size (US$ Bn) Forecast, by Architecture Industry
    9.5. Global Deep Learning Market Analysis, by Architecture Industry
    9.6. Global Deep Learning Market Attractiveness Analysis, by Architecture Industry

    10. Global Deep Learning Market Analysis and Forecast, by End Use Industry
    10.1. Introduction and Definition
    10.2. Key Findings
    10.3. Global Deep Learning Market Value Share Analysis, by End Use Industry
    10.4. Global Deep Learning Market Size (US$ Bn) Forecast, by End Use Industry
    10.5. Global Deep Learning Market Analysis, by End Use Industry
    10.6. Global Deep Learning Market Attractiveness Analysis, by End Use Industry

    11. Global Deep Learning Market Analysis, by Region
    11.1. Global Deep Learning Market Value Share Analysis, by Region
    11.2. Global Deep Learning Market Size (US$ Bn) Forecast, by Region
    11.3. Global Deep Learning Market Attractiveness Analysis, by Region

    12. North America Deep Learning Market Analysis
    12.1. Key Findings
    12.2. North America Deep Learning Market Overview
    12.3. North America Deep Learning Market Value Share Analysis, by Component
    12.4. North America Deep Learning Market Forecast, by Component
    12.4.1. Hardware
    12.4.2. Software
    12.5. North America Deep Learning Market Value Share Analysis, by Application
    12.6. North America Deep Learning Market Forecast, by Application
    12.6.1. Speech Recognition
    12.6.2. Image Recognition
    12.6.3. Data Mining
    12.6.4. Drug Discovery
    12.6.5. Driver Assistance
    12.6.6. Others
    12.7. North America Deep Learning Market Value Share Analysis, by Architecture Industry
    12.8. North America Deep Learning Market Forecast, by Architecture Industry
    12.8.1. RNN
    12.8.2. CNN
    12.8.3. DBN
    12.8.4. DSN
    12.8.5. GRU
    12.9. North America Deep Learning Market Value Share Analysis, by End Use Industry
    12.10. North America Deep Learning Market Forecast, by End Use Industry
    12.10.1. Healthcare
    12.10.2. Automotive
    12.10.3. Media & Entertainment
    12.10.4. BFSI
    12.10.5. Other
    12.11. North America Deep Learning Market Value Share Analysis, by Country
    12.12. North America Deep Learning Market Forecast, by Country
    12.12.1. U.S.
    12.12.2. Canada
    12.13. North America Deep Learning Market Analysis, by Country
    12.14. U.S. Deep Learning Market Forecast, by Component
    12.14.1. Hardware
    12.14.2. Software
    12.15. U.S. Deep Learning Market Forecast, by Application
    12.15.1. Speech Recognition
    12.15.2. Image Recognition
    12.15.3. Data Mining
    12.15.4. Drug Discovery
    12.15.5. Driver Assistance
    12.15.6. Others
    12.16. U.S. Deep Learning Market Forecast, by Architecture Industry
    12.16.1. RNN
    12.16.2. CNN
    12.16.3. DBN
    12.16.4. DSN
    12.16.5. GRU
    12.17. U.S. Deep Learning Market Forecast, by End Use Industry
    12.17.1. Healthcare
    12.17.2. Automotive
    12.17.3. Media & Entertainment
    12.17.4. BFSI
    12.17.5. Other
    12.18. Canada Deep Learning Market Forecast, by Component
    12.18.1. Hardware
    12.18.2. Software
    12.19. Canada Deep Learning Market Forecast, by Application
    12.19.1. Speech Recognition
    12.19.2. Image Recognition
    12.19.3. Data Mining
    12.19.4. Drug Discovery
    12.19.5. Driver Assistance
    12.19.6. Others
    12.20. Canada Deep Learning Market Forecast, by Architecture Industry
    12.20.1. RNN
    12.20.2. CNN
    12.20.3. DBN
    12.20.4. DSN
    12.20.5. GRU
    12.21. Canada Deep Learning Market Forecast, by End Use Industry
    12.21.1. Healthcare
    12.21.2. Automotive
    12.21.3. Media & Entertainment
    12.21.4. BFSI
    12.21.5. Other
    12.22. North America Deep Learning Market Attractiveness Analysis
    12.22.1. By Component
    12.22.2. By Application
    12.22.3. By Architecture Industry
    12.22.4. By End Use Industry
    12.23. PEST Analysis
    12.24. Key Trends
    12.25. Key Development

    13. Europe Deep Learning Market Analysis
    13.1. Key Findings
    13.2. Europe Deep Learning Market Overview
    13.3. Europe Deep Learning Market Value Share Analysis, by Component
    13.4. Europe Deep Learning Market Forecast, by Component
    13.4.1. Hardware
    13.4.2. Software
    13.5. Europe Deep Learning Market Value Share Analysis, by Application
    13.6. Europe Deep Learning Market Forecast, by Application
    13.6.1. Speech Recognition
    13.6.2. Image Recognition
    13.6.3. Data Mining
    13.6.4. Drug Discovery
    13.6.5. Driver Assistance
    13.6.6. Others
    13.7. Europe Deep Learning Market Value Share Analysis, by Architecture Industry
    13.8. Europe Deep Learning Market Forecast, by Architecture Industry
    13.8.1. RNN
    13.8.2. CNN
    13.8.3. DBN
    13.8.4. DSN
    13.8.5. GRU
    13.9. Europe Deep Learning Market Value Share Analysis, by End Use Industry
    13.10. Europe Deep Learning Market Forecast, by End Use Industry
    13.10.1. Healthcare
    13.10.2. Automotive
    13.10.3. Media & Entertainment
    13.10.4. BFSI
    13.10.5. Other
    13.11. Europe Deep Learning Market Value Share Analysis, by Country
    13.12. Europe Deep Learning Market Forecast, by Country
    13.12.1. Germany
    13.12.2. U.K.
    13.12.3. France
    13.12.4. Italy
    13.12.5. Spain
    13.12.6. Rest of Europe
    13.13. Europe Deep Learning Market Analysis, by Country
    13.14. Germany Deep Learning Market Forecast, by Component
    13.14.1. Hardware
    13.14.2. Software
    13.15. Germany Deep Learning Market Forecast, by Application
    13.15.1. Speech Recognition
    13.15.2. Image Recognition
    13.15.3. Data Mining
    13.15.4. Drug Discovery
    13.15.5. Driver Assistance
    13.15.6. Others
    13.16. Germany Deep Learning Market Forecast, by Architecture Industry
    13.16.1. RNN
    13.16.2. CNN
    13.16.3. DBN
    13.16.4. DSN
    13.16.5. GRU
    13.17. Germany Deep Learning Market Forecast, by End Use Industry
    13.17.1. Healthcare
    13.17.2. Automotive
    13.17.3. Media & Entertainment
    13.17.4. BFSI
    13.17.5. Other
    13.18. U.K. Deep Learning Market Forecast, by Component
    13.18.1. Hardware
    13.18.2. Software
    13.19. U.K. Deep Learning Market Forecast, by Application
    13.19.1. Speech Recognition
    13.19.2. Image Recognition
    13.19.3. Data Mining
    13.19.4. Drug Discovery
    13.19.5. Driver Assistance
    13.19.6. Others
    13.20. U.K. Deep Learning Market Forecast, by Architecture Industry
    13.20.1. RNN
    13.20.2. CNN
    13.20.3. DBN
    13.20.4. DSN
    13.20.5. GRU
    13.21. U.K. Deep Learning Market Forecast, by End Use Industry
    13.21.1. Healthcare
    13.21.2. Automotive
    13.21.3. Media & Entertainment
    13.21.4. BFSI
    13.21.5. Other
    13.22. France Deep Learning Market Forecast, by Component
    13.22.1. Hardware
    13.22.2. Software
    13.23. France Deep Learning Market Forecast, by Application
    13.23.1. Speech Recognition
    13.23.2. Image Recognition
    13.23.3. Data Mining
    13.23.4. Drug Discovery
    13.23.5. Driver Assistance
    13.23.6. Others
    13.24. France Deep Learning Market Forecast, by Architecture Industry
    13.24.1. RNN
    13.24.2. CNN
    13.24.3. DBN
    13.24.4. DSN
    13.24.5. GRU
    13.25. France Deep Learning Market Forecast, by End Use Industry
    13.25.1. Healthcare
    13.25.2. Automotive
    13.25.3. Media & Entertainment
    13.25.4. BFSI
    13.25.5. Other
    13.26. Italy Deep Learning Market Forecast, by Component
    13.26.1. Hardware
    13.26.2. Software
    13.27. Italy Deep Learning Market Forecast, by Application
    13.27.1. Speech Recognition
    13.27.2. Image Recognition
    13.27.3. Data Mining
    13.27.4. Drug Discovery
    13.27.5. Driver Assistance
    13.27.6. Others
    13.28. Italy Deep Learning Market Forecast, by Architecture Industry
    13.28.1. RNN
    13.28.2. CNN
    13.28.3. DBN
    13.28.4. DSN
    13.28.5. GRU
    13.29. Italy Deep Learning Market Forecast, by End Use Industry
    13.29.1. Healthcare
    13.29.2. Automotive
    13.29.3. Media & Entertainment
    13.29.4. BFSI
    13.29.5. Other
    13.30. Spain Deep Learning Market Forecast, by Component
    13.30.1. Hardware
    13.30.2. Software
    13.31. Spain Deep Learning Market Forecast, by Application
    13.31.1. Speech Recognition
    13.31.2. Image Recognition
    13.31.3. Data Mining
    13.31.4. Drug Discovery
    13.31.5. Driver Assistance
    13.31.6. Others
    13.32. Spain Deep Learning Market Forecast, by Architecture Industry
    13.32.1. RNN
    13.32.2. CNN
    13.32.3. DBN
    13.32.4. DSN
    13.32.5. GRU
    13.33. Spain Deep Learning Market Forecast, by End Use Industry
    13.33.1. Healthcare
    13.33.2. Automotive
    13.33.3. Media & Entertainment
    13.33.4. BFSI
    13.33.5. Other
    13.34. Rest of Europe Deep Learning Market Forecast, by Component
    13.34.1. Hardware
    13.34.2. Software
    13.35. Rest of Europe Deep Learning Market Forecast, by Application
    13.35.1. Speech Recognition
    13.35.2. Image Recognition
    13.35.3. Data Mining
    13.35.4. Drug Discovery
    13.35.5. Driver Assistance
    13.35.6. Others
    13.36. Rest of Europe Deep Learning Market Forecast, by Architecture Industry
    13.36.1. RNN
    13.36.2. CNN
    13.36.3. DBN
    13.36.4. DSN
    13.36.5. GRU
    13.37. Rest Of Europe Deep Learning Market Forecast, by End Use Industry
    13.37.1. Healthcare
    13.37.2. Automotive
    13.37.3. Media & Entertainment
    13.37.4. BFSI
    13.37.5. Other
    13.38. Europe Deep Learning Market Attractiveness Analysis
    13.38.1. By Component
    13.38.2. By Application
    13.38.3. By Architecture Industry
    13.38.4. By End Use Industry
    13.39. PEST Analysis
    13.40. Key Trends
    13.41. Key Development

    14. Asia Pacific Deep Learning Market Analysis
    14.1. Key Findings
    14.2. Asia Pacific Deep Learning Market Overview
    14.3. Asia Pacific Deep Learning Market Value Share Analysis, by Component
    14.4. Asia Pacific Deep Learning Market Forecast, by Component
    14.4.1. Hardware
    14.4.2. Software
    14.5. Asia Pacific Deep Learning Market Value Share Analysis, by Application
    14.6. Asia Pacific Deep Learning Market Forecast, by Application
    14.6.1. Speech Recognition
    14.6.2. Image Recognition
    14.6.3. Data Mining
    14.6.4. Drug Discovery
    14.6.5. Driver Assistance
    14.6.6. Others
    14.7. Asia Pacific Deep Learning Market Value Share Analysis, by Architecture Industry
    14.8. Asia Pacific Deep Learning Market Forecast, by Architecture Industry
    14.8.1. RNN
    14.8.2. CNN
    14.8.3. DBN
    14.8.4. DSN
    14.8.5. GRU
    14.9. Asia Pacific Deep Learning Market Value Share Analysis, by End Use Industry
    14.10. Asia Pacific Deep Learning Market Forecast, by End Use Industry
    14.10.1. Healthcare
    14.10.2. Automotive
    14.10.3. Media & Entertainment
    14.10.4. BFSI
    14.10.5. Other
    14.11. Asia Pacific Deep Learning Market Value Share Analysis, by Country
    14.12. Asia Pacific Deep Learning Market Forecast, by Country
    14.12.1. China
    14.12.2. India
    14.12.3. Japan
    14.12.4. ASEAN
    14.12.5. Rest of Asia Pacific
    14.13. Asia Pacific Deep Learning Market Analysis, by Country
    14.14. China Deep Learning Market Forecast, by Component
    14.14.1. Hardware
    14.14.2. Software
    14.15. China Deep Learning Market Forecast, by Application
    14.15.1. Speech Recognition
    14.15.2. Image Recognition
    14.15.3. Data Mining
    14.15.4. Drug Discovery
    14.15.5. Driver Assistance
    14.15.6. Others
    14.16. China Deep Learning Market Forecast, by Architecture Industry
    14.16.1. RNN
    14.16.2. CNN
    14.16.3. DBN
    14.16.4. DSN
    14.16.5. GRU
    14.17. China Deep Learning Market Forecast, by End Use Industry
    14.17.1. Healthcare
    14.17.2. Automotive
    14.17.3. Media & Entertainment
    14.17.4. BFSI
    14.17.5. Other
    14.18. India Deep Learning Market Forecast, by Component
    14.18.1. Hardware
    14.18.2. Software
    14.19. India Deep Learning Market Forecast, by Application
    14.19.1. Speech Recognition
    14.19.2. Image Recognition
    14.19.3. Data Mining
    14.19.4. Drug Discovery
    14.19.5. Driver Assistance
    14.19.6. Others
    14.20. India Deep Learning Market Forecast, by Architecture Industry
    14.20.1. RNN
    14.20.2. CNN
    14.20.3. DBN
    14.20.4. DSN
    14.20.5. GRU
    14.21. India Deep Learning Market Forecast, by End Use Industry
    14.21.1. Healthcare
    14.21.2. Automotive
    14.21.3. Media & Entertainment
    14.21.4. BFSI
    14.21.5. Other
    14.22. Japan Deep Learning Market Forecast, by Component
    14.22.1. Hardware
    14.22.2. Software
    14.23. Japan Deep Learning Market Forecast, by Application
    14.23.1. Speech Recognition
    14.23.2. Image Recognition
    14.23.3. Data Mining
    14.23.4. Drug Discovery
    14.23.5. Driver Assistance
    14.23.6. Others
    14.24. Japan Deep Learning Market Forecast, by Architecture Industry
    14.24.1. RNN
    14.24.2. CNN
    14.24.3. DBN
    14.24.4. DSN
    14.24.5. GRU
    14.25. Japan Deep Learning Market Forecast, by End Use Industry
    14.25.1. Healthcare
    14.25.2. Automotive
    14.25.3. Media & Entertainment
    14.25.4. BFSI
    14.25.5. Other
    14.26. ASEAN Deep Learning Market Forecast, by Component
    14.26.1. Hardware
    14.26.2. Software
    14.27. ASEAN Deep Learning Market Forecast, by Application
    14.27.1. Speech Recognition
    14.27.2. Image Recognition
    14.27.3. Data Mining
    14.27.4. Drug Discovery
    14.27.5. Driver Assistance
    14.27.6. Others
    14.28. ASEAN Deep Learning Market Forecast, by Architecture Industry
    14.28.1. RNN
    14.28.2. CNN
    14.28.3. DBN
    14.28.4. DSN
    14.28.5. GRU
    14.29. ASEAN Deep Learning Market Forecast, by End Use Industry
    14.29.1. Healthcare
    14.29.2. Automotive
    14.29.3. Media & Entertainment
    14.29.4. BFSI
    14.29.5. Other
    14.30. Rest of Asia Pacific Deep Learning Market Forecast, by Component
    14.30.1. Hardware
    14.30.2. Software
    14.31. Rest of Asia Pacific Deep Learning Market Forecast, by Application
    14.31.1. Speech Recognition
    14.31.2. Image Recognition
    14.31.3. Data Mining
    14.31.4. Drug Discovery
    14.31.5. Driver Assistance
    14.31.6. Others
    14.32. Rest of Asia Pacific Deep Learning Market Forecast, by Architecture Industry
    14.32.1. RNN
    14.32.2. CNN
    14.32.3. DBN
    14.32.4. DSN
    14.32.5. GRU
    14.33. Rest of Asia Pacific Deep Learning Market Forecast, by End Use Industry
    14.33.1. Healthcare
    14.33.2. Automotive
    14.33.3. Media & Entertainment
    14.33.4. BFSI
    14.33.5. Other
    14.34. Asia Pacific Deep Learning Market Attractiveness Analysis
    14.34.1. By Component
    14.34.2. By Application
    14.34.3. By Architecture Industry
    14.34.4. By End Use Industry
    14.35. PEST Analysis
    14.36. Key Trends
    14.37. Key Development

    15. Middle East & Africa Deep Learning Market Analysis
    15.1. Key Findings
    15.2. Middle East & Africa Deep Learning Market Overview
    15.3. Middle East & Africa Deep Learning Market Value Share Analysis, by Component
    15.4. Middle East & Africa Deep Learning Market Forecast, by Component
    15.4.1. Hardware
    15.4.2. Software
    15.5. Middle East & Africa Deep Learning Market Value Share Analysis, by Application
    15.6. Middle East & Africa Deep Learning Market Forecast, by Application
    15.6.1. Speech Recognition
    15.6.2. Image Recognition
    15.6.3. Data Mining
    15.6.4. Drug Discovery
    15.6.5. Driver Assistance
    15.6.6. Others
    15.7. Middle East & Africa Deep Learning Market Value Share Analysis, by Architecture Industry
    15.8. Middle East & Africa Deep Learning Market Forecast, by Architecture Industry
    15.8.1. RNN
    15.8.2. CNN
    15.8.3. DBN
    15.8.4. DSN
    15.8.5. GRU
    15.9. Middle East & Africa Deep Learning Market Value Share Analysis, by End Use Industry
    15.10. Middle East & Africa Deep Learning Market Forecast, by End Use Industry
    15.10.1. Healthcare
    15.10.2. Automotive
    15.10.3. Media & Entertainment
    15.10.4. BFSI
    15.10.5. Other
    15.11. Middle East & Africa Deep Learning Market Value Share Analysis, by Country
    15.12. Middle East & Africa Deep Learning Market Forecast, by Country
    15.12.1. GCC
    15.12.2. South Africa
    15.12.3. Rest of Middle East & Africa
    15.13. Middle East & Africa Deep Learning Market Analysis, by Country
    15.14. GCC Deep Learning Market Forecast, by Component
    15.14.1. Hardware
    15.14.2. Software
    15.15. GCC Deep Learning Market Forecast, by Application
    15.15.1. Speech Recognition
    15.15.2. Image Recognition
    15.15.3. Data Mining
    15.15.4. Drug Discovery
    15.15.5. Driver Assistance
    15.15.6. Others
    15.16. GCC Deep Learning Market Forecast, by Architecture Industry
    15.16.1. RNN
    15.16.2. CNN
    15.16.3. DBN
    15.16.4. DSN
    15.16.5. GRU
    15.17. GCC Deep Learning Market Forecast, by End Use Industry
    15.17.1. Healthcare
    15.17.2. Automotive
    15.17.3. Media & Entertainment
    15.17.4. BFSI
    15.17.5. Other
    15.18. South Africa Deep Learning Market Forecast, by Component
    15.18.1. Hardware
    15.18.2. Software
    15.19. South Africa Deep Learning Market Forecast, by Application
    15.19.1. Speech Recognition
    15.19.2. Image Recognition
    15.19.3. Data Mining
    15.19.4. Drug Discovery
    15.19.5. Driver Assistance
    15.19.6. Others
    15.20. South Africa Deep Learning Market Forecast, by Architecture Industry
    15.20.1. RNN
    15.20.2. CNN
    15.20.3. DBN
    15.20.4. DSN
    15.20.5. GRU
    15.21. South Africa Deep Learning Market Forecast, by End Use Industry
    15.21.1. Healthcare
    15.21.2. Automotive
    15.21.3. Media & Entertainment
    15.21.4. BFSI
    15.21.5. Other
    15.22. Rest of Middle East & Africa Deep Learning Market Forecast, by Component
    15.22.1. Hardware
    15.22.2. Software
    15.23. Rest of Middle East & Africa Deep Learning Market Forecast, by Application
    15.23.1. Speech Recognition
    15.23.2. Image Recognition
    15.23.3. Data Mining
    15.23.4. Drug Discovery
    15.23.5. Driver Assistance
    15.23.6. Others
    15.24. Rest of Middle East & Africa Deep Learning Market Forecast, by Architecture Industry
    15.24.1. RNN
    15.24.2. CNN
    15.24.3. DBN
    15.24.4. DSN
    15.24.5. GRU
    15.25. Rest of Middle East & Africa Deep Learning Market Forecast, by End Use Industry
    15.25.1. Healthcare
    15.25.2. Automotive
    15.25.3. Media & Entertainment
    15.25.4. BFSI
    15.25.5. Other
    15.26. Middle East & Africa Deep Learning Market Attractiveness Analysis
    15.26.1. By Component
    15.26.2. By Application
    15.26.3. By Architecture Industry
    15.26.4. By End Use Industry
    15.27. PEST Analysis
    15.28. Key Trends
    15.29. Key Development

    16. South America Deep Learning Market Analysis
    16.1. Key Findings
    16.2. South America Deep Learning Market Overview
    16.3. South America Deep Learning Market Value Share Analysis, by Component
    16.4. South America Deep Learning Market Forecast, by Component
    16.4.1. Hardware
    16.4.2. Software
    16.5. South America Deep Learning Market Value Share Analysis, by Application
    16.6. South America Deep Learning Market Forecast, by Application
    16.6.1. Speech Recognition
    16.6.2. Image Recognition
    16.6.3. Data Mining
    16.6.4. Drug Discovery
    16.6.5. Driver Assistance
    16.6.6. Others
    16.7. South America Deep Learning Market Value Share Analysis, by Architecture Industry
    16.8. South America Deep Learning Market Forecast, by Architecture Industry
    16.8.1. RNN
    16.8.2. CNN
    16.8.3. DBN
    16.8.4. DSN
    16.8.5. GRU
    16.9. South America Deep Learning Market Value Share Analysis, by End Use Industry
    16.10. South America Deep Learning Market Forecast, by End Use Industry
    16.10.1. Healthcare
    16.10.2. Automotive
    16.10.3. Media & Entertainment
    16.10.4. BFSI
    16.10.5. Other
    16.11. South America Deep Learning Market Value Share Analysis, by Country
    16.12. South America Deep Learning Market Forecast, by Country
    16.12.1. Brazil
    16.12.2. Mexico
    16.12.3. Rest of South America
    16.13. South America Deep Learning Market Analysis, by Country
    16.14. Brazil Deep Learning Market Forecast, by Component
    16.14.1. Hardware
    16.14.2. Software
    16.15. Brazil Deep Learning Market Forecast, by Application
    16.15.1. Speech Recognition
    16.15.2. Image Recognition
    16.15.3. Data Mining
    16.15.4. Drug Discovery
    16.15.5. Driver Assistance
    16.15.6. Others
    16.16. Brazil Deep Learning Market Forecast, by Architecture Industry
    16.16.1. RNN
    16.16.2. CNN
    16.16.3. DBN
    16.16.4. DSN
    16.16.5. GRU
    16.17. Brazil Deep Learning Market Forecast, by End Use Industry
    16.17.1. Healthcare
    16.17.2. Automotive
    16.17.3. Media & Entertainment
    16.17.4. BFSI
    16.17.5. Other
    16.18. Mexico Deep Learning Market Forecast, by Component
    16.18.1. Hardware
    16.18.2. Software
    16.19. Mexico Deep Learning Market Forecast, by Application
    16.19.1. Speech Recognition
    16.19.2. Image Recognition
    16.19.3. Data Mining
    16.19.4. Drug Discovery
    16.19.5. Driver Assistance
    16.19.6. Others
    16.20. Mexico Deep Learning Market Forecast, by Architecture Industry
    16.20.1. RNN
    16.20.2. CNN
    16.20.3. DBN
    16.20.4. DSN
    16.20.5. GRU
    16.21. Mexico Deep Learning Market Forecast, by End Use Industry
    16.21.1. Healthcare
    16.21.2. Automotive
    16.21.3. Media & Entertainment
    16.21.4. BFSI
    16.21.5. Other
    16.22. Rest of South America Deep Learning Market Forecast, by Component
    16.22.1. Hardware
    16.22.2. Software
    16.23. Rest of South America Deep Learning Market Forecast, by Application
    16.23.1. Speech Recognition
    16.23.2. Image Recognition
    16.23.3. Data Mining
    16.23.4. Drug Discovery
    16.23.5. Driver Assistance
    16.23.6. Others
    16.24. Rest of South America Deep Learning Market Forecast, by Architecture Industry
    16.24.1. RNN
    16.24.2. CNN
    16.24.3. DBN
    16.24.4. DSN
    16.24.5. GRU
    16.25. Rest of South America Deep Learning Market Forecast, by End Use Industry
    16.25.1. Healthcare
    16.25.2. Automotive
    16.25.3. Media & Entertainment
    16.25.4. BFSI
    16.25.5. Other
    16.26. South America Deep Learning Market Attractiveness Analysis
    16.26.1. By Component
    16.26.2. By Application
    16.26.3. By Architecture Industry
    16.26.4. By End Use Industry
    16.27. PEST Analysis
    16.28. Key Trends
    16.29. Key Development

    17. Company Profiles
    17.1. Market Share Analysis, by Company
    17.2. Competition Matrix
    17.2.1. Competitive Benchmarking of key players by price, presence, market share, Applications and R&D investment
    17.2.2. New Product Launches and Product Enhancements
    17.2.3. Market Consolidation
    17.2.3.1. M&A by Regions, Investment and Applications
    17.2.3.2. M&A Key Players, Forward Integration and Backward
    Integration
    17.3. Company Profiles: Key Players
    17.3.1. Advanced Micro Devices, Inc
    17.3.1.1. Company Overview
    17.3.1.2. Financial Overview
    17.3.1.3. Product Portfolio
    17.3.1.4. Business Strategy
    17.3.1.5. Recent Developments
    17.3.1.6. Company Footprint
    17.3.2. Arm Ltd.
    17.3.3. Baidu Inc.
    17.3.4. Clairifai Inc.
    17.3.5. Enlitic
    17.3.6. General Vision Inc.
    17.3.7. Google Inc.
    17.3.8. Hewlett Packard
    17.3.9. IBM Corporation
    17.3.10. Intel Corporation
    17.3.11. Microsoft Corporation
    17.3.12. Nvidia Corporation
    17.3.13. Qualcomm Technologies Inc.
    17.3.14. Sensory Inc.
    17.3.15. Skymind
    17.3.16. Alphabet Inc.
    17.3.17. Micron Technology Inc.
    17.3.18. Amazon Web Services

    18. Primary Key Insights

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