Global Deep Learning Market Overview

The Global Deep Learning Market size is expected to grow from USD 118.02 billion in 2023 to USD 2361.15 billion by 2032, at a CAGR of 39.5% during the forecast period (2024-2032).

Deep learning also referred to as deep structured learning is a subset of machine learning and artificial intelligence. It is an important component of data science which contain statistics and predictive modeling. It is very useful for those data scientists who are engaged in collecting, analyzing, and interpreting huge amounts of data, and data learning assists to make this process makes faster and easier. Deep learning manages several applications of artificial intelligence and services that develop automation, performing analytical, and physical tasks without interruption of humans. Deep learning contains deep neural networks, deep reinforcement learning, deep belief networks, recurrent neural networks, and convolutional neural networks that have been used in various fields like speech recognition, computer vision, natural language processing, drug design, and medical image analysis, machine translation, and bioinformatics. These applications support the growth of the market.

Market Dynamics and Factors of Deep Learning Market

Drivers:

The Growing Use of Autonomous Vehicle and Healthcare Industries

The deep learning technology used in autonomous vehicles is self-driving. The main role of deep learning in an autonomous vehicle is to interpret complex vision tasks, enhance perception, localize itself in the environment, and actuate kinematic maneuvers. This assures road safety and also commute easily. In addition to this, deep learning is also used in the healthcare sector. It is already seen in medical imaging solutions, and chatbots that can help to find the patient’s symptoms, and identify the types of cancer, rare diseases, and pathology types. Deep learning also analyzes the electronic health records including structured and unstructured data it contains laboratory test results, clinical notes, diagnoses, and medications. It also offers personalized medical treatments and helps to find the errors in the prescription and correct them. These all beneficial factors propel the growth of the market of deep learning in the forecast year.

Restraints:

Highly Expensive Deep Learning Technology

Deep learning technology is too expensive because complex data models are a key factor hindering the market growth. For deep learning technology multicore high-performing graphics processing units (GPUs) and tensor processing units (TPUs) are required. They are expensive and use a large amount of energy. This increase the cost of deep learning technology which is restricted the growth of the market of deep learning in the forecast period.

Opportunity:

Growing Demand for Human-Machine Interaction

The human-machine interaction demand is rising which offers a lucrative opportunity for the market of deep learning. The human-machine interaction (HMI) is termed communication and interaction between the human and a machine through a user interface. The human-machine interface provides numerous advantages such as enhancing the efficiency of the machine, the ability to effectively control any system or device, high efficiency in recording the data, and assistance to translate the industrial control system data into readable and visual representations by human and decline the hardware cost. These are all beneficial factors that provide the profitable opportunity for the growth of the deep learning market in the analysis year.

Segmentation Analysis of Deep Learning Market

By Type, the software segment is projected to have the maximum market share in deep learning in the analysis period. This is owing to the growing adoption of software solutions in different applications like ATMs that can read checks, voice and image recognition systems, and smartphone assistants on social networks. In addition to this, the number of software that provide the ads on many websites. These are driving the growth of the market of deep learning. Several companies engaged in the manufacturing and development system and provide the software online as well as offline based on applications. Most major players offer the installation and training of these systems with online assistance and post-maintenance of software. These factors contributed to the market growth of the software segment in the deep learning market.

By Application, the Image recognition segment dominates the deep learning market. It is one of the crucial fields of image processing and computer vision. The rising demand for pattern recognition, optical character recognition, code recognition, facial recognition, object recognition, and digital image processing propels the growth of the market. Deep learning techniques assist in the development of natural language processing and visual data mining. The data mining technology is used in sentiment analysis, machine translation, fingerprint identification, cybersecurity, and bioinformatics. These all remarkable factors propel the growth of deep learning in the image recognition application.

By End User, the Automotive segment is anticipated to have maximum growth in the deep learning market in the analysis year. Deep learning technology has a number of automotive applications such as visual inspection in manufacturing, social media analytics, autonomous driving, robots, smart machines, and conversational user interface that are contributed to the growth of the market of deep learning. Additionally, deep learning is the subtype of Artificial intelligence (AI). The growing penetration rate of AI technology in the manufacturing, design, supply chain, production, post-production, driver assistance, and driver risk assessment systems also supported the growth of the deep learning market over the forecast period.

Regional Analysis of Deep Learning Market

North America has projected too high market growth in deep learning owing to growing investments in artificial intelligence and neural networks by major players in this region. For the purpose of image recognition, voice recognition, data mining, signal recognition, and diagnostic purposes cognition, the adoption of DL application models is increasing. The digital businesses in this region are rising rapidly. These factors supported the growth of the deep learning market over the forecast period. Mexico, Canada, and the United States are the dominating country in this region. This is due to the quick adoption of advanced technology in this country, government support, and growing application of deep learning in the healthcare, automotive, IT and telecommunications, aerospace & defense sectors in this country. These all factors contribute to the growth of the market.

Europe is the second dominating region in the deep learning market. The growing adoption of artificial intelligence and neural networks among the various businesses in this region is a key factor that propels the growth of the market of this region. The UK country is dominating the market in the Europe region. Increase the application of deep learning for image recognition, data mining, and signal recognition purposes. Government investment is rising in the development of deep learning technology. These factors help the growth of the deep learning market. Additionally, the growing use in healthcare, IT and telecommunications, and automotive sectors in this region also supported the growth of the market over the forecast period.

The Asia Pacific has significant growth in the market of deep learning. This is owing to the growing penetration rate and development in deep learning technology. Increasing the digitalization, image recognition platforms, and voice recognition platforms support the growth of the market in this region. In addition to this, the governments of various countries in this region increase their investment in the advancement of technology, and the growing adoption of AI and machine learning technology also boosts the market growth. The rising popularity of deep learning in electrical items such as smartphones, tablets, PCs, and in healthcare, automotive products. China and India are the dominating countries in this region because of the rising research and development of advanced technology.

Covid 19 impact Analysis on Deep Learning Market

COVID-19 starts in Wuhan (China) in December 2019 and has since rapidly spread throughout the world. In terms of confirmed cases and reported deaths, the US, India, Brazil, Russia, France, the UK, Turkey, Italy, and Spain are among the countries that have been most severely impacted. Due to lockdowns, travel restrictions, and business closures, COVID-19 has had an impact on the businesses and industries of numerous nations. The Covid 19 spread has positively impacted the deep learning market. The adoption of developed technology such as artificial intelligence, learning techniques, machine learning, and DevOps during the pandemic period. Additionally, anti-money laundering (AML), fraud detection solutions, and various other solutions are highly demanded at the time of covid 19. Thus, the Deep learning market had significant growth during the pandemic.

Top Key Players Covered In Deep Learning Market

• Advanced Micro Devices (US)
• ARM Ltd (UK)
• Clarifai (US)
• Entilic(US)
• Google (US)
• HyperVerge(US)
• IBM (US)
• Intel (US)
• Microsoft (US)
• NVIDIA (US) and other major players.

Key Industry Development In The Deep Learning Market:

• In March 2024, Nikon Industrial Metrology Business Unit released AI Reconstruction, an innovative 3D computed tomography (CT) reconstruction software solution powered by artificial intelligence that lifts the traditional trade-off between scan speed and image quality. By applying Deep Learning techniques to enhance image quality, Nikon’s breakthrough technology enables rapid results and superior analysis.
• In November 2023, GE HealthCare’s Artificial Intelligence (AI) models predict patient response to immunotherapies with 70 to 80 percent accuracy, based on a pan-cancer cohort, according to findings to be presented at the Society for Immunotherapy of Cancer (SITC) in San Diego, U.S., by GE HealthCare, Vanderbilt University Medical Center (VUMC) and the University Medicine Essen (UME), Germany.

Chapter 1: Introduction
 1.1 Research Objectives
 1.2 Research Methodology
 1.3 Research Process
 1.4 Scope and Coverage
  1.4.1 Market Definition
  1.4.2 Key Questions Answered
 1.5 Market Segmentation

Chapter 2:Executive Summary

Chapter 3:Growth Opportunities By Segment
 3.1 By Type
 3.2 By Application
 3.3 By End-User

Chapter 4: Market Landscape
 4.1 Porter's Five Forces Analysis
  4.1.1 Bargaining Power of Supplier
  4.1.2 Threat of New Entrants
  4.1.3 Threat of Substitutes
  4.1.4 Competitive Rivalry
  4.1.5 Bargaining Power Among Buyers
 4.2 Industry Value Chain Analysis
 4.3 Market Dynamics
  4.3.1 Drivers
  4.3.2 Restraints
  4.3.3 Opportunities
  4.5.4 Challenges
 4.4 Pestle Analysis
 4.5 Technological Roadmap
 4.6 Regulatory Landscape
 4.7 SWOT Analysis
 4.8 Price Trend Analysis
 4.9 Patent Analysis
 4.10 Analysis of the Impact of Covid-19
  4.10.1 Impact on the Overall Market
  4.10.2 Impact on the Supply Chain
  4.10.3 Impact on the Key Manufacturers
  4.10.4 Impact on the Pricing

Chapter 5: Deep Learning Market by Type
 5.1 Deep Learning Market Overview Snapshot and Growth Engine
 5.2 Deep Learning Market Overview
 5.3 Software
  5.3.1 Introduction and Market Overview
  5.3.2 Historic and Forecasted Market Size (2016-2032F)
  5.3.3 Key Market Trends, Growth Factors and Opportunities
  5.3.4 Software: Grographic Segmentation
 5.4 Hardware
  5.4.1 Introduction and Market Overview
  5.4.2 Historic and Forecasted Market Size (2016-2032F)
  5.4.3 Key Market Trends, Growth Factors and Opportunities
  5.4.4 Hardware: Grographic Segmentation
 5.5 Service
  5.5.1 Introduction and Market Overview
  5.5.2 Historic and Forecasted Market Size (2016-2032F)
  5.5.3 Key Market Trends, Growth Factors and Opportunities
  5.5.4 Service: Grographic Segmentation

Chapter 6: Deep Learning Market by Application
 6.1 Deep Learning Market Overview Snapshot and Growth Engine
 6.2 Deep Learning Market Overview
 6.3 Signal Recognition
  6.3.1 Introduction and Market Overview
  6.3.2 Historic and Forecasted Market Size (2016-2028F)
  6.3.3 Key Market Trends, Growth Factors and Opportunities
  6.3.4 Signal Recognition: Grographic Segmentation
 6.4 Image Recognition
  6.4.1 Introduction and Market Overview
  6.4.2 Historic and Forecasted Market Size (2016-2032F)
  6.4.3 Key Market Trends, Growth Factors and Opportunities
  6.4.4 Image Recognition: Grographic Segmentation
 6.5 Others
  6.5.1 Introduction and Market Overview
  6.5.2 Historic and Forecasted Market Size (2016-2032F)
  6.5.3 Key Market Trends, Growth Factors and Opportunities
  6.5.4 Others: Grographic Segmentation

Chapter 7: Deep Learning Market by End-User
 7.1 Deep Learning Market Overview Snapshot and Growth Engine
 7.2 Deep Learning Market Overview
 7.3 Aerospace & Defense
  7.3.1 Introduction and Market Overview
  7.3.2 Historic and Forecasted Market Size (2016-2032F)
  7.3.3 Key Market Trends, Growth Factors and Opportunities
  7.3.4 Aerospace & Defense: Grographic Segmentation
 7.4 Automotive
  7.4.1 Introduction and Market Overview
  7.4.2 Historic and Forecasted Market Size (2016-2028F)
  7.4.3 Key Market Trends, Growth Factors and Opportunities
  7.4.4 Automotive: Grographic Segmentation
 7.5 Manufacturing
  7.5.1 Introduction and Market Overview
  7.5.2 Historic and Forecasted Market Size (2016-2032F)
  7.5.3 Key Market Trends, Growth Factors and Opportunities
  7.5.4 Manufacturing: Grographic Segmentation
 7.6 Healthcare
  7.6.1 Introduction and Market Overview
  7.6.2 Historic and Forecasted Market Size (2016-2032F)
  7.6.3 Key Market Trends, Growth Factors and Opportunities
  7.6.4 Healthcare: Grographic Segmentation

Chapter 8: Company Profiles and Competitive Analysis
 8.1 Competitive Landscape
  8.1.1 Competitive Positioning
  8.1.2 Deep Learning Sales and Market Share By Players
  8.1.3 Industry BCG Matrix
  8.1.4 Ansoff Matrix
  8.1.5 Deep Learning Industry Concentration Ratio (CR5 and HHI)
  8.1.6 Top 5 Deep Learning Players Market Share
  8.1.7 Mergers and Acquisitions
  8.1.8 Business Strategies By Top Players
 8.2 ADVANCED MICRO DEVICES
  8.2.1 Company Overview
  8.2.2 Key Executives
  8.2.3 Company Snapshot
  8.2.4 Operating Business Segments
  8.2.5 Product Portfolio
  8.2.6 Business Performance
  8.2.7 Key Strategic Moves and Recent Developments
  8.2.8 SWOT Analysis
 8.3 ARM LTD
 8.4 CLARIFAI
 8.5 ENTILIC
 8.6 GOOGLE
 8.7 HYPERVERGE
 8.8 IBM
 8.9 INTEL
 8.10 MICROSOFT
 8.11 NVIDIA
 8.12 OTHER MAJOR PLAYERS

Chapter 9: Global Deep Learning Market Analysis, Insights and Forecast, 2016-2032
 9.1 Market Overview
 9.2 Historic and Forecasted Market Size By Type
  9.2.1 Software
  9.2.2 Hardware
  9.2.3 Service
 9.3 Historic and Forecasted Market Size By Application
  9.3.1 Signal Recognition
  9.3.2 Image Recognition
  9.3.3 Others
 9.4 Historic and Forecasted Market Size By End-User
  9.4.1 Aerospace & Defense
  9.4.2 Automotive
  9.4.3 Manufacturing
  9.4.4 Healthcare

Chapter 10: North America Deep Learning Market Analysis, Insights and Forecast, 2016-2032
 10.1 Key Market Trends, Growth Factors and Opportunities
 10.2 Impact of Covid-19
 10.3 Key Players
 10.4 Key Market Trends, Growth Factors and Opportunities
 10.4 Historic and Forecasted Market Size By Type
  10.4.1 Software
  10.4.2 Hardware
  10.4.3 Service
 10.5 Historic and Forecasted Market Size By Application
  10.5.1 Signal Recognition
  10.5.2 Image Recognition
  10.5.3 Others
 10.6 Historic and Forecasted Market Size By End-User
  10.6.1 Aerospace & Defense
  10.6.2 Automotive
  10.6.3 Manufacturing
  10.6.4 Healthcare
 10.7 Historic and Forecast Market Size by Country
  10.7.1 U.S.
  10.7.2 Canada
  10.7.3 Mexico

Chapter 11: Europe Deep Learning Market Analysis, Insights and Forecast, 2016-2032
 11.1 Key Market Trends, Growth Factors and Opportunities
 11.2 Impact of Covid-19
 11.3 Key Players
 11.4 Key Market Trends, Growth Factors and Opportunities
 11.4 Historic and Forecasted Market Size By Type
  11.4.1 Software
  11.4.2 Hardware
  11.4.3 Service
 11.5 Historic and Forecasted Market Size By Application
  11.5.1 Signal Recognition
  11.5.2 Image Recognition
  11.5.3 Others
 11.6 Historic and Forecasted Market Size By End-User
  11.6.1 Aerospace & Defense
  11.6.2 Automotive
  11.6.3 Manufacturing
  11.6.4 Healthcare
 11.7 Historic and Forecast Market Size by Country
  11.7.1 Germany
  11.7.2 U.K.
  11.7.3 France
  11.7.4 Italy
  11.7.5 Russia
  11.7.6 Spain
  11.7.7 Rest of Europe

Chapter 12: Asia-Pacific Deep Learning Market Analysis, Insights and Forecast, 2016-2032
 12.1 Key Market Trends, Growth Factors and Opportunities
 12.2 Impact of Covid-19
 12.3 Key Players
 12.4 Key Market Trends, Growth Factors and Opportunities
 12.4 Historic and Forecasted Market Size By Type
  12.4.1 Software
  12.4.2 Hardware
  12.4.3 Service
 12.5 Historic and Forecasted Market Size By Application
  12.5.1 Signal Recognition
  12.5.2 Image Recognition
  12.5.3 Others
 12.6 Historic and Forecasted Market Size By End-User
  12.6.1 Aerospace & Defense
  12.6.2 Automotive
  12.6.3 Manufacturing
  12.6.4 Healthcare
 12.7 Historic and Forecast Market Size by Country
  12.7.1 China
  12.7.2 India
  12.7.3 Japan
  12.7.4 Singapore
  12.7.5 Australia
  12.7.6 New Zealand
  12.7.7 Rest of APAC

Chapter 13: Middle East & Africa Deep Learning Market Analysis, Insights and Forecast, 2016-2032
 13.1 Key Market Trends, Growth Factors and Opportunities
 13.2 Impact of Covid-19
 13.3 Key Players
 13.4 Key Market Trends, Growth Factors and Opportunities
 13.4 Historic and Forecasted Market Size By Type
  13.4.1 Software
  13.4.2 Hardware
  13.4.3 Service
 13.5 Historic and Forecasted Market Size By Application
  13.5.1 Signal Recognition
  13.5.2 Image Recognition
  13.5.3 Others
 13.6 Historic and Forecasted Market Size By End-User
  13.6.1 Aerospace & Defense
  13.6.2 Automotive
  13.6.3 Manufacturing
  13.6.4 Healthcare
 13.7 Historic and Forecast Market Size by Country
  13.7.1 Turkey
  13.7.2 Saudi Arabia
  13.7.3 Iran
  13.7.4 UAE
  13.7.5 Africa
  13.7.6 Rest of MEA

Chapter 14: South America Deep Learning Market Analysis, Insights and Forecast, 2016-2032
 14.1 Key Market Trends, Growth Factors and Opportunities
 14.2 Impact of Covid-19
 14.3 Key Players
 14.4 Key Market Trends, Growth Factors and Opportunities
 14.4 Historic and Forecasted Market Size By Type
  14.4.1 Software
  14.4.2 Hardware
  14.4.3 Service
 14.5 Historic and Forecasted Market Size By Application
  14.5.1 Signal Recognition
  14.5.2 Image Recognition
  14.5.3 Others
 14.6 Historic and Forecasted Market Size By End-User
  14.6.1 Aerospace & Defense
  14.6.2 Automotive
  14.6.3 Manufacturing
  14.6.4 Healthcare
 14.7 Historic and Forecast Market Size by Country
  14.7.1 Brazil
  14.7.2 Argentina
  14.7.3 Rest of SA

Chapter 15 Investment Analysis

Chapter 16 Analyst Viewpoint and Conclusion

LIST OF TABLES

TABLE 001. EXECUTIVE SUMMARY
TABLE 002. DEEP LEARNING MARKET BARGAINING POWER OF SUPPLIERS
TABLE 003. DEEP LEARNING MARKET BARGAINING POWER OF CUSTOMERS
TABLE 004. DEEP LEARNING MARKET COMPETITIVE RIVALRY
TABLE 005. DEEP LEARNING MARKET THREAT OF NEW ENTRANTS
TABLE 006. DEEP LEARNING MARKET THREAT OF SUBSTITUTES
TABLE 007. DEEP LEARNING MARKET BY TYPE
TABLE 008. SOFTWARE MARKET OVERVIEW (2016-2028)
TABLE 009. HARDWARE MARKET OVERVIEW (2016-2028)
TABLE 010. SERVICE MARKET OVERVIEW (2016-2028)
TABLE 011. DEEP LEARNING MARKET BY APPLICATION
TABLE 012. SIGNAL RECOGNITION MARKET OVERVIEW (2016-2028)
TABLE 013. IMAGE RECOGNITION MARKET OVERVIEW (2016-2028)
TABLE 014. OTHERS MARKET OVERVIEW (2016-2028)
TABLE 015. DEEP LEARNING MARKET BY END-USER
TABLE 016. AEROSPACE & DEFENSE MARKET OVERVIEW (2016-2028)
TABLE 017. AUTOMOTIVE MARKET OVERVIEW (2016-2028)
TABLE 018. MANUFACTURING MARKET OVERVIEW (2016-2028)
TABLE 019. HEALTHCARE MARKET OVERVIEW (2016-2028)
TABLE 020. NORTH AMERICA DEEP LEARNING MARKET, BY TYPE (2016-2028)
TABLE 021. NORTH AMERICA DEEP LEARNING MARKET, BY APPLICATION (2016-2028)
TABLE 022. NORTH AMERICA DEEP LEARNING MARKET, BY END-USER (2016-2028)
TABLE 023. N DEEP LEARNING MARKET, BY COUNTRY (2016-2028)
TABLE 024. EUROPE DEEP LEARNING MARKET, BY TYPE (2016-2028)
TABLE 025. EUROPE DEEP LEARNING MARKET, BY APPLICATION (2016-2028)
TABLE 026. EUROPE DEEP LEARNING MARKET, BY END-USER (2016-2028)
TABLE 027. DEEP LEARNING MARKET, BY COUNTRY (2016-2028)
TABLE 028. ASIA PACIFIC DEEP LEARNING MARKET, BY TYPE (2016-2028)
TABLE 029. ASIA PACIFIC DEEP LEARNING MARKET, BY APPLICATION (2016-2028)
TABLE 030. ASIA PACIFIC DEEP LEARNING MARKET, BY END-USER (2016-2028)
TABLE 031. DEEP LEARNING MARKET, BY COUNTRY (2016-2028)
TABLE 032. MIDDLE EAST & AFRICA DEEP LEARNING MARKET, BY TYPE (2016-2028)
TABLE 033. MIDDLE EAST & AFRICA DEEP LEARNING MARKET, BY APPLICATION (2016-2028)
TABLE 034. MIDDLE EAST & AFRICA DEEP LEARNING MARKET, BY END-USER (2016-2028)
TABLE 035. DEEP LEARNING MARKET, BY COUNTRY (2016-2028)
TABLE 036. SOUTH AMERICA DEEP LEARNING MARKET, BY TYPE (2016-2028)
TABLE 037. SOUTH AMERICA DEEP LEARNING MARKET, BY APPLICATION (2016-2028)
TABLE 038. SOUTH AMERICA DEEP LEARNING MARKET, BY END-USER (2016-2028)
TABLE 039. DEEP LEARNING MARKET, BY COUNTRY (2016-2028)
TABLE 040. ADVANCED MICRO DEVICES: SNAPSHOT
TABLE 041. ADVANCED MICRO DEVICES: BUSINESS PERFORMANCE
TABLE 042. ADVANCED MICRO DEVICES: PRODUCT PORTFOLIO
TABLE 043. ADVANCED MICRO DEVICES: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 043. ARM LTD: SNAPSHOT
TABLE 044. ARM LTD: BUSINESS PERFORMANCE
TABLE 045. ARM LTD: PRODUCT PORTFOLIO
TABLE 046. ARM LTD: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 046. CLARIFAI: SNAPSHOT
TABLE 047. CLARIFAI: BUSINESS PERFORMANCE
TABLE 048. CLARIFAI: PRODUCT PORTFOLIO
TABLE 049. CLARIFAI: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 049. ENTILIC: SNAPSHOT
TABLE 050. ENTILIC: BUSINESS PERFORMANCE
TABLE 051. ENTILIC: PRODUCT PORTFOLIO
TABLE 052. ENTILIC: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 052. GOOGLE: SNAPSHOT
TABLE 053. GOOGLE: BUSINESS PERFORMANCE
TABLE 054. GOOGLE: PRODUCT PORTFOLIO
TABLE 055. GOOGLE: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 055. HYPERVERGE: SNAPSHOT
TABLE 056. HYPERVERGE: BUSINESS PERFORMANCE
TABLE 057. HYPERVERGE: PRODUCT PORTFOLIO
TABLE 058. HYPERVERGE: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 058. IBM: SNAPSHOT
TABLE 059. IBM: BUSINESS PERFORMANCE
TABLE 060. IBM: PRODUCT PORTFOLIO
TABLE 061. IBM: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 061. INTEL: SNAPSHOT
TABLE 062. INTEL: BUSINESS PERFORMANCE
TABLE 063. INTEL: PRODUCT PORTFOLIO
TABLE 064. INTEL: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 064. MICROSOFT: SNAPSHOT
TABLE 065. MICROSOFT: BUSINESS PERFORMANCE
TABLE 066. MICROSOFT: PRODUCT PORTFOLIO
TABLE 067. MICROSOFT: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 067. NVIDIA: SNAPSHOT
TABLE 068. NVIDIA: BUSINESS PERFORMANCE
TABLE 069. NVIDIA: PRODUCT PORTFOLIO
TABLE 070. NVIDIA: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 070. OTHER MAJOR PLAYERS: SNAPSHOT
TABLE 071. OTHER MAJOR PLAYERS: BUSINESS PERFORMANCE
TABLE 072. OTHER MAJOR PLAYERS: PRODUCT PORTFOLIO
TABLE 073. OTHER MAJOR PLAYERS: KEY STRATEGIC MOVES AND DEVELOPMENTS

LIST OF FIGURES

FIGURE 001. YEARS CONSIDERED FOR ANALYSIS
FIGURE 002. SCOPE OF THE STUDY
FIGURE 003. DEEP LEARNING MARKET OVERVIEW BY REGIONS
FIGURE 004. PORTER'S FIVE FORCES ANALYSIS
FIGURE 005. BARGAINING POWER OF SUPPLIERS
FIGURE 006. COMPETITIVE RIVALRYFIGURE 007. THREAT OF NEW ENTRANTS
FIGURE 008. THREAT OF SUBSTITUTES
FIGURE 009. VALUE CHAIN ANALYSIS
FIGURE 010. PESTLE ANALYSIS
FIGURE 011. DEEP LEARNING MARKET OVERVIEW BY TYPE
FIGURE 012. SOFTWARE MARKET OVERVIEW (2016-2028)
FIGURE 013. HARDWARE MARKET OVERVIEW (2016-2028)
FIGURE 014. SERVICE MARKET OVERVIEW (2016-2028)
FIGURE 015. DEEP LEARNING MARKET OVERVIEW BY APPLICATION
FIGURE 016. SIGNAL RECOGNITION MARKET OVERVIEW (2016-2028)
FIGURE 017. IMAGE RECOGNITION MARKET OVERVIEW (2016-2028)
FIGURE 018. OTHERS MARKET OVERVIEW (2016-2028)
FIGURE 019. DEEP LEARNING MARKET OVERVIEW BY END-USER
FIGURE 020. AEROSPACE & DEFENSE MARKET OVERVIEW (2016-2028)
FIGURE 021. AUTOMOTIVE MARKET OVERVIEW (2016-2028)
FIGURE 022. MANUFACTURING MARKET OVERVIEW (2016-2028)
FIGURE 023. HEALTHCARE MARKET OVERVIEW (2016-2028)
FIGURE 024. NORTH AMERICA DEEP LEARNING MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 025. EUROPE DEEP LEARNING MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 026. ASIA PACIFIC DEEP LEARNING MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 027. MIDDLE EAST & AFRICA DEEP LEARNING MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 028. SOUTH AMERICA DEEP LEARNING MARKET OVERVIEW BY COUNTRY (2016-2028)