The global deep learning market revenue was around US$ 16.9 billion in 2022 and is estimated to reach US$ 302.7 billion by 2031, growing at a compound annual growth rate (CAGR) of 37.8% during the forecast period from 2023 to 2031.
Deep learning is a type of machine learning and artificial intelligence technology that imitates human behaviour to develop human brain cells-generated information. The technology is beneficial in performing a variety of tasks and identifying patterns in text, photos, audio, and other data. Additionally, it is used to automate tasks like photo annotation and voice transcription that often need human intelligence.
Market Driving Factors
The growing adoption of chatbots boosts the market growth. The deep learning has applications in machine translation, chatbots, and service bots. A trained deep neural network (DNN) decodes a word or a sentence without the usage of a large database. In addition, DNNs make more better and accurate outcomes than conventional machine translation techniques, which enhances system performance. As a result, deep learning algorithms can be used in service bots and chatbots to improve customer service and lower call centre workload.
The increasing Importance of big data analytics boosts the market growth. Big data is widely adopted by several business organizations as they are collecting a substantial amount of data according to the needs of the organization. This data generation is expected to rise further with technologies such as 5G. Thus, it is estimated that deep learning will discover applications in big data analytics to extract sophisticated patterns from an enormous amount of data. Deep learning can understand and assess a substantial amount of unsupervised data. Therefore, it is considered a suitable tool for big data analytics.
Technical difficulties and shortage of accuracy limit the market growth.
Regional Analysis
North America dominated the market. This can be attributed to the availability of high-performance graphics processing units (GPUs) and specialized hardware accelerators that increase the deployment and development of deep learning models, allowing faster training and inference times in the region. Furthermore, the availability of settled IT infrastructure and high investments in the region drive the market growth.
Segmentation Insights
Component Insight
The software segment holds the largest shares in the market. This is due to the surge in the adoption of software solutions in several applications, such as ATMs that read checks, smartphone assistants, image and voice recognition software on social networks, and software that offers up ads on many websites. Such, these factors drive the segment growth.
Application Insight
The image recognition segment dominates the market. This is due to the increasing demand for optical character recognition, pattern recognition, facial recognition, code recognition, digital image processing, and object recognition.
The data mining segment is estimated to dominate the market. This is because to the fact that deep learning models can automatically identify difficult patterns, relationships, and correlations in data. This is particularly valuable for data mining tasks like image recognition, fraud detection, and natural language processing.
Industry Vertical Insight
The healthcare segment dominates the market. Digital transformation in the healthcare industry is estimated to offer lucrative opportunities for innovative technologies such as deep learning, AI, and data analytics to intervene in the industry. In addition, deep learning can be utilized in predictive analytics, such as early detection of illnesses, identifying clinical risk and its drivers, and predicting future hospitalization.
Prominent Companies
Segmentation Outline
The global deep learning market segmentation focuses on Component, Application, Industry Vertical, and Region.
By Component
By Application
By Industry Vertical
By Region
[TABLE OF CONTENTS]
1 INTRODUCTION OF GLOBAL DEEP LEARNING MARKET
1.1 OVERVIEW OF THE MARKET
1.2 SCOPE OF REPORT
1.3 ASSUMPTIONS
2 EXECUTIVE SUMMARY: DEEP LEARNING MARKET
3 RESEARCH METHODOLOGY
3.1 DATA MINING
3.2 VALIDATION
3.3 PRIMARY INTERVIEWS
3.4 LIST OF DATA SOURCES
3.5 ANALYST TOOLS AND MODELS
4 GLOBAL DEEP LEARNING MARKET OUTLOOK
4.1 OVERVIEW
4.2 MARKET DYNAMICS AND TRENDS
4.2.1 DRIVERS
4.2.2 RESTRAINTS
4.2.3 OPPORTUNITIES
4.3 PORTERS FIVE FORCE ANALYSIS
4.4 VALUE CHAIN ANALYSIS
4.5 MARKET GROWTH AND OUTLOOK
4.5.1 PRICE TREND ANALYSIS
4.5.2 OPPORTUNITY SHARE
5 GLOBAL DEEP LEARNING MARKET, BY COMPONENT
5.1 OVERVIEW
5.2 SOFTWARE
5.3 SERVICE
5.4 HARDWARE
6 GLOBAL DEEP LEARNING MARKET, BY APPLICATION
6.1 OVERVIEW
6.2 IMAGE RECOGNITION
6.3 SIGNAL RECOGNITION
6.4 DATA MINING
6.5 OTHERS
7 GLOBAL DEEP LEARNING MARKET, BY INDUSTRY VERTICAL
7.1 OVERVIEW
7.2 SECURITY
7.3 MARKETING
7.4 AUTOMOTIVE
7.5 RETAIL AND E-COMMERCE
7.6 HEALTHCARE
7.7 MANUFACTURING
7.8 LAW
7.9 OTHERS
8 GLOBAL DEEP LEARNING MARKET, BY GEOGRAPHY
8.1 OVERVIEW
8.2 NORTH AMERICA
8.2.1 NORTH AMERICA MARKET SNAPSHOT
8.2.2 U.S.
8.2.3 CANADA
8.2.4 MEXICO
8.3 EUROPE
8.3.1 EUROPE MARKET SNAPSHOT
8.3.2 WESTERN EUROPE
8.3.2.1 THE UK
8.3.2.2 GERMANY
8.3.2.3 FRANCE
8.3.2.4 ITALY
8.3.2.5 SPAIN
8.3.2.6 REST OF WESTERN EUROPE
8.3.3 EASTERN EUROPE
8.3.3.1 POLAND
8.3.3.2 RUSSIA
8.3.3.3 REST OF EASTERN EUROPE
8.4 ASIA PACIFIC
8.4.1 ASIA PACIFIC MARKET SNAPSHOT
8.4.2 CHINA
8.4.3 JAPAN
8.4.4 INDIA
8.4.5 AUSTRALIA & NEW ZEALAND
8.4.6 ASEAN
8.4.7 REST OF ASIA PACIFIC
8.5 MIDDLE EAST & AFRICA
8.5.1 MIDDLE EAST & AFRICA MARKET SNAPSHOT
8.5.2 UAE
8.5.3 SAUDI ARABIA
8.5.4 SOUTH AFRICA
8.5.5 REST OF MEA
8.6 SOUTH AMERICA
8.6.1 SOUTH AMERICA MARKET SNAPSHOT
8.6.2 BRAZIL
8.6.3 ARGENTINA
8.6.4 REST OF SOUTH AMERICA
9 GLOBAL DEEP LEARNING MARKET COMPETITIVE LANDSCAPE
9.1 OVERVIEW
9.2 COMPANY MARKET RANKING
9.3 KEY DEVELOPMENT STRATEGIES
9.4 COMPETITIVE DASHBOARD
9.5 PRODUCT MAPPING
9.6 TOP PLAYER POSITIONING, 2022
9.7 COMPETITIVE HEATMAP
9.8 TOP WINNING STRATEGIES
10 COMPANY PROFILES
10.1 GOOGLE LLC
10.1.1 OVERVIEW
10.1.2 FINANCIAL PERFORMANCE
10.1.3 PRODUCT OUTLOOK
10.1.4 KEY DEVELOPMENTS
10.1.5 KEY STRATEGIC MOVES AND DEVELOPMENTS
10.2 QUALCOMM TECHNOLOGIES INC
10.2.1 OVERVIEW
10.2.2 FINANCIAL PERFORMANCE
10.2.3 PRODUCT OUTLOOK
10.2.4 KEY DEVELOPMENTS
10.2.5 KEY STRATEGIC MOVES AND DEVELOPMENTS
10.3 NVIDIA CORPORATION
10.3.1 OVERVIEW
10.3.2 FINANCIAL PERFORMANCE
10.3.3 PRODUCT OUTLOOK
10.3.4 KEY DEVELOPMENTS
10.3.5 KEY STRATEGIC MOVES AND DEVELOPMENTS
10.4 MICROSOFT CORPORATION
10.4.1 OVERVIEW
10.4.2 FINANCIAL PERFORMANCE
10.4.3 PRODUCT OUTLOOK
10.4.4 KEY DEVELOPMENTS
10.4.5 KEY STRATEGIC MOVES AND DEVELOPMENTS
10.5 AMAZON WEB SERVICES INC
10.5.1 OVERVIEW
10.5.2 FINANCIAL PERFORMANCE
10.5.3 PRODUCT OUTLOOK
10.5.4 KEY DEVELOPMENTS
10.5.5 KEY STRATEGIC MOVES AND DEVELOPMENTS
10.6 SAMSUNG
10.6.1 OVERVIEW
10.6.2 FINANCIAL PERFORMANCE
10.6.3 PRODUCT OUTLOOK
10.6.4 KEY DEVELOPMENTS
10.6.5 KEY STRATEGIC MOVES AND DEVELOPMENTS
10.7 XILINX
10.7.1 OVERVIEW
10.7.2 FINANCIAL PERFORMANCE
10.7.3 PRODUCT OUTLOOK
10.7.4 KEY DEVELOPMENTS
10.7.5 KEY STRATEGIC MOVES AND DEVELOPMENTS
10.8 IBM CORPORATION
10.8.1 OVERVIEW
10.8.2 FINANCIAL PERFORMANCE
10.8.3 PRODUCT OUTLOOK
10.8.4 KEY DEVELOPMENTS
10.8.5 KEY STRATEGIC MOVES AND DEVELOPMENTS
10.9 INTEL CORPORATION
10.9.1 OVERVIEW
10.9.2 FINANCIAL PERFORMANCE
10.9.3 PRODUCT OUTLOOK
10.9.4 KEY DEVELOPMENTS
10.9.5 KEY STRATEGIC MOVES AND DEVELOPMENTS
10.10 ADVANCED MICRO DEVICES INC
10.10.1 OVERVIEW
10.10.2 FINANCIAL PERFORMANCE
10.10.3 PRODUCT OUTLOOK
10.10.4 KEY DEVELOPMENTS
10.10.5 KEY STRATEGIC MOVES AND DEVELOPMENTS
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