Product Code: SR1523A141_Report
The global deep learning market size reached US$ 17.2 Billion in 2022. Looking forward, IMARC Group expects the market to reach US$ 113.0 Billion by 2028, exhibiting a growth rate (CAGR) of 38.2% during 2023-2028.
Deep learning, or deep structured learning, is a division of machine learning that uses layered algorithmic models for analyzing data. It is a crucial component of data science, which uses statistics and predictive modeling for collecting, analyzing and interpreting large amounts of information. It also involves the use of artificial intelligence (AI) to imitate the functioning of the human brain while processing data, forming patterns and making decisions. This technology is commonly used in image recognition tools, natural language processing (NLP) and speech recognition software, self-driving vehicles and language translation services and finds extensive applications across the retail, healthcare, automotive, agriculture, security and manufacturing industries.
The expanding information technology (IT) industry, along with the rising trend of digitalization, is one of the key factors driving the growth of the market. In comparison to the traditionally used computing systems, deep learning algorithms can automatically intercept available data points, which enhances the efficiency and accuracy of the decision-making process. Furthermore, deep learning solutions are widely employed for cybersecurity, database management and fraud detection systems. They are also utilized for processing medical images for disease diagnosis and drug discovery and offering virtual patient assistance in the healthcare sector, which is contributing to the widespread adoption of the technology. Other factors, including its integration with big data analytics and cloud computing, along with extensive research and development (R&D) activities to develop improved hardware and software processing solutions for deep learning, are projected to drive the market in the coming years.
Key Market Segmentation:
- IMARC Group provides an analysis of the key trends in each sub-segment of the global deep learning market report, along with forecasts at the global, regional and country level from 2023-2028. Our report has categorized the market based on product type, application, end-use industry and architecture.
Breakup by Product Type:
- Software
- Services
- Hardware
Breakup by Application:
- Image Recognition
- Signal Recognition
- Data Mining
- Others
Breakup by End-Use Industry:
- Security
- Manufacturing
- Retail
- Automotive
- Healthcare
- Agriculture
- Others
Breakup by Architecture:
Breakup by Region:
- North America
- United States
- Canada
- Asia Pacific
- China
- Japan
- India
- South Korea
- Australia
- Indonesia
- Others
- Europe
- Germany
- France
- United Kingdom
- Italy
- Spain
- Russia
- Others
- Latin America
- Brazil
- Mexico
- Others
- Middle East and Africa
Competitive Landscape:
- The report has also analysed the competitive landscape of the market with some of the key players being Amazon Web Services (AWS), Google Inc., IBM, Intel, Micron Technology, Microsoft Corporation, Nvidia, Qualcomm, Samsung Electronics, Sensory Inc., Pathmind, Inc., Xilinx, etc.
Key Questions Answered in This Report:
- 1. What was the size of the global deep learning market in 2022?
- 2. What is the expected growth rate of the global deep learning market during 2023-2028?
- 3. What has been the impact of COVID-19 on the global deep learning market?
- 4. What are the key factors driving the global deep learning market?
- 5. What is the breakup of the global deep learning market based on the product type?
- 6. What is the breakup of the global deep learning market based on the application?
- 7. What is the breakup of the global deep learning market based on the end-use industry?
- 8. What are the key regions in the global deep learning market?
- 9. Who are the key players/companies in the global deep learning market?
Table of Contents
1 Preface
2 Scope and Methodology
- 2.1 Objectives of the Study
- 2.2 Stakeholders
- 2.3 Data Sources
- 2.3.1 Primary Sources
- 2.3.2 Secondary Sources
- 2.4 Market Estimation
- 2.4.1 Bottom-Up Approach
- 2.4.2 Top-Down Approach
- 2.5 Forecasting Methodology
3 Executive Summary
4 Introduction
- 4.1 Overview
- 4.2 Key Industry Trends
5 Global Deep Learning Market
- 5.1 Market Overview
- 5.2 Market Performance
- 5.3 Impact of COVID-19
- 5.4 Market Forecast
6 Market Breakup by Product Type
- 6.1 Software
- 6.1.1 Market Trends
- 6.1.2 Market Forecast
- 6.2 Services
- 6.2.1 Market Trends
- 6.2.2 Market Forecast
- 6.3 Hardware
- 6.3.1 Market Trends
- 6.3.2 Market Forecast
7 Market Breakup by Application
- 7.1 Image Recognition
- 7.1.1 Market Trends
- 7.1.2 Market Forecast
- 7.2 Signal Recognition
- 7.2.1 Market Trends
- 7.2.2 Market Forecast
- 7.3 Data Mining
- 7.3.1 Market Trends
- 7.3.2 Market Forecast
- 7.4 Others
- 7.4.1 Market Trends
- 7.4.2 Market Forecast
8 Market Breakup by End-Use Industry
- 8.1 Security
- 8.1.1 Market Trends
- 8.1.2 Market Forecast
- 8.2 Manufacturing
- 8.2.1 Market Trends
- 8.2.2 Market Forecast
- 8.3 Retail
- 8.3.1 Market Trends
- 8.3.2 Market Forecast
- 8.4 Automotive
- 8.4.1 Market Trends
- 8.4.2 Market Forecast
- 8.5 Healthcare
- 8.5.1 Market Trends
- 8.5.2 Market Forecast
- 8.6 Agriculture
- 8.6.1 Market Trends
- 8.6.2 Market Forecast
- 8.7 Others
- 8.7.1 Market Trends
- 8.7.2 Market Forecast
9 Market Breakup by Architecture
- 9.1 RNN
- 9.1.1 Market Trends
- 9.1.2 Market Forecast
- 9.2 CNN
- 9.2.1 Market Trends
- 9.2.2 Market Forecast
- 9.3 DBN
- 9.3.1 Market Trends
- 9.3.2 Market Forecast
- 9.4 DSN
- 9.4.1 Market Trends
- 9.4.2 Market Forecast
- 9.5 GRU
- 9.5.1 Market Trends
- 9.5.2 Market Forecast
10 Market Breakup by Region
- 10.1 North America
- 10.1.1 United States
- 10.1.1.1 Market Trends
- 10.1.1.2 Market Forecast
- 10.1.2 Canada
- 10.1.2.1 Market Trends
- 10.1.2.2 Market Forecast
- 10.2 Asia Pacific
- 10.2.1 China
- 10.2.1.1 Market Trends
- 10.2.1.2 Market Forecast
- 10.2.2 Japan
- 10.2.2.1 Market Trends
- 10.2.2.2 Market Forecast
- 10.2.3 India
- 10.2.3.1 Market Trends
- 10.2.3.2 Market Forecast
- 10.2.4 South Korea
- 10.2.4.1 Market Trends
- 10.2.4.2 Market Forecast
- 10.2.5 Australia
- 10.2.5.1 Market Trends
- 10.2.5.2 Market Forecast
- 10.2.6 Indonesia
- 10.2.6.1 Market Trends
- 10.2.6.2 Market Forecast
- 10.2.7 Others
- 10.2.7.1 Market Trends
- 10.2.7.2 Market Forecast
- 10.3 Europe
- 10.3.1 Germany
- 10.3.1.1 Market Trends
- 10.3.1.2 Market Forecast
- 10.3.2 France
- 10.3.2.1 Market Trends
- 10.3.2.2 Market Forecast
- 10.3.3 United Kingdom
- 10.3.3.1 Market Trends
- 10.3.3.2 Market Forecast
- 10.3.4 Italy
- 10.3.4.1 Market Trends
- 10.3.4.2 Market Forecast
- 10.3.5 Spain
- 10.3.5.1 Market Trends
- 10.3.5.2 Market Forecast
- 10.3.6 Russia
- 10.3.6.1 Market Trends
- 10.3.6.2 Market Forecast
- 10.3.7 Others
- 10.3.7.1 Market Trends
- 10.3.7.2 Market Forecast
- 10.4 Latin America
- 10.4.1 Brazil
- 10.4.1.1 Market Trends
- 10.4.1.2 Market Forecast
- 10.4.2 Mexico
- 10.4.2.1 Market Trends
- 10.4.2.2 Market Forecast
- 10.4.3 Others
- 10.4.3.1 Market Trends
- 10.4.3.2 Market Forecast
- 10.5 Middle East and Africa
- 10.5.1 Market Trends
- 10.5.2 Market Breakup by Country
- 10.5.3 Market Forecast
11 SWOT Analysis
- 11.1 Overview
- 11.2 Strengths
- 11.3 Weaknesses
- 11.4 Opportunities
- 11.5 Threats
12 Value Chain Analysis
13 Porters Five Forces Analysis
- 13.1 Overview
- 13.2 Bargaining Power of Buyers
- 13.3 Bargaining Power of Suppliers
- 13.4 Degree of Competition
- 13.5 Threat of New Entrants
- 13.6 Threat of Substitutes
14 Competitive Landscape
- 14.1 Market Structure
- 14.2 Key Players
- 14.3 Profiles of Key Players
- 14.3.1 Amazon Web Services (AWS)
- 14.3.1.1 Company Overview
- 14.3.1.2 Product Portfolio
- 14.3.2 Google Inc.
- 14.3.2.1 Company Overview
- 14.3.2.2 Product Portfolio
- 14.3.2.3 SWOT Analysis
- 14.3.3 IBM
- 14.3.3.1 Company Overview
- 14.3.3.2 Product Portfolio
- 14.3.4 Intel
- 14.3.4.1 Company Overview
- 14.3.4.2 Product Portfolio
- 14.3.4.3 Financials
- 14.3.4.4 SWOT Analysis
- 14.3.5 Micron Technology
- 14.3.5.1 Company Overview
- 14.3.5.2 Product Portfolio
- 14.3.5.3 Financials
- 14.3.5.4 SWOT Analysis
- 14.3.6 Microsoft Corporation
- 14.3.6.1 Company Overview
- 14.3.6.2 Product Portfolio
- 14.3.6.3 Financials
- 14.3.6.4 SWOT Analysis
- 14.3.7 Nvidia
- 14.3.7.1 Company Overview
- 14.3.7.2 Product Portfolio
- 14.3.7.3 Financials
- 14.3.7.4 SWOT Analysis
- 14.3.8 Qualcomm
- 14.3.8.1 Company Overview
- 14.3.8.2 Product Portfolio
- 14.3.8.3 Financials
- 14.3.8.4 SWOT Analysis
- 14.3.9 Samsung Electronics
- 14.3.9.1 Company Overview
- 14.3.9.2 Product Portfolio
- 14.3.10 Sensory Inc.
- 14.3.10.1 Company Overview
- 14.3.10.2 Product Portfolio
- 14.3.11 Pathmind Inc.
- 14.3.11.1 Company Overview
- 14.3.11.2 Product Portfolio
- 14.3.12 Xilinx
- 14.3.12.1 Company Overview
- 14.3.12.2 Product Portfolio
- 14.3.12.3 Financials
- 14.3.12.4 SWOT Analysis