The deep learning market has been segmented on the basis of offerings, applications, end-user industries, and geographies. In terms of offerings, software holds the largest share of the deep learning market. Also, the market for services is expected to grow at the highest CAGR from 2018 to 2023. The increasing adoption of deep learning software solutions in various applications, such as smartphone assistants, ATMs that read checks, voice and image recognition software on social network, and software that serves up ads on many websites, is driving the growth of machine learning technology in the deep learning market. Most companies that manufacture and develop deep learning systems and related software provide both online and offline support, depending on the application. Several companies provide installation, training, and support pertaining to these systems, along with online assistance and post-maintenance of software and required services.
The global Deep Learning market was valued at US$ 2671.2 million in 2023 and is anticipated to reach US$ 11910 million by 2030, witnessing a CAGR of 23.6% during the forecast period 2024-2030.
North American market for Deep Learning is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
Asia-Pacific market for Deep Learning is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The global market for Deep Learning in Healthcare is estimated to increase from $ million in 2023 to $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The major global companies of Deep Learning include Amazon Web Services (AWS), Google, IBM, Intel, Micron Technology, Microsoft, Nvidia, Qualcomm and Samsung, etc. In 2023, the world's top three vendors accounted for approximately % of the revenue.
This report aims to provide a comprehensive presentation of the global market for Deep Learning, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Deep Learning.
Report Scope
The Deep Learning market size, estimations, and forecasts are provided in terms of revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. This report segments the global Deep Learning market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the Deep Learning companies, new entrants, and industry chain related companies in this market with information on the revenues, sales volume, and average price for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
麻豆原创 Segmentation
By Company
Amazon Web Services (AWS)
Google
IBM
Intel
Micron Technology
Microsoft
Nvidia
Qualcomm
Samsung
Sensory Inc.
Skymind
Xilinx
AMD
General Vision
Graphcore
Mellanox Technologies
Huawei Technologies
Fujitsu
Baidu
Mythic
Adapteva
Koniku
Segment by Type
Hardware
Software
Services
Segment by Application
Healthcare
Manufacturing
Automotive
Agriculture
Retail
Security
Human Resources
麻豆原创ing
By Region
North America
United States
Canada
Europe
Germany
France
UK
Italy
Russia
Nordic Countries
Rest of Europe
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by Type, by Application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of Deep Learning companies鈥� competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.
Please Note - This is an on demand report and will be delivered in 2 business days (48 hours) post payment.
1 Report Overview
1.1 Study Scope
1.2 麻豆原创 Analysis by Type
1.2.1 Global Deep Learning 麻豆原创 Size Growth Rate by Type: 2019 VS 2023 VS 2030
1.2.2 Hardware
1.2.3 Software
1.2.4 Services
1.3 麻豆原创 by Application
1.3.1 Global Deep Learning 麻豆原创 Growth by Application: 2019 VS 2023 VS 2030
1.3.2 Healthcare
1.3.3 Manufacturing
1.3.4 Automotive
1.3.5 Agriculture
1.3.6 Retail
1.3.7 Security
1.3.8 Human Resources
1.3.9 麻豆原创ing
1.4 Study Objectives
1.5 Years Considered
1.6 Years Considered
2 Global Growth Trends
2.1 Global Deep Learning 麻豆原创 Perspective (2019-2030)
2.2 Deep Learning Growth Trends by Region
2.2.1 Global Deep Learning 麻豆原创 Size by Region: 2019 VS 2023 VS 2030
2.2.2 Deep Learning Historic 麻豆原创 Size by Region (2019-2024)
2.2.3 Deep Learning Forecasted 麻豆原创 Size by Region (2025-2030)
2.3 Deep Learning 麻豆原创 Dynamics
2.3.1 Deep Learning Industry Trends
2.3.2 Deep Learning 麻豆原创 Drivers
2.3.3 Deep Learning 麻豆原创 Challenges
2.3.4 Deep Learning 麻豆原创 Restraints
3 Competition Landscape by Key Players
3.1 Global Top Deep Learning Players by Revenue
3.1.1 Global Top Deep Learning Players by Revenue (2019-2024)
3.1.2 Global Deep Learning Revenue 麻豆原创 Share by Players (2019-2024)
3.2 Global Deep Learning 麻豆原创 Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Players Covered: Ranking by Deep Learning Revenue
3.4 Global Deep Learning 麻豆原创 Concentration Ratio
3.4.1 Global Deep Learning 麻豆原创 Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by Deep Learning Revenue in 2023
3.5 Deep Learning Key Players Head office and Area Served
3.6 Key Players Deep Learning Product Solution and Service
3.7 Date of Enter into Deep Learning 麻豆原创
3.8 Mergers & Acquisitions, Expansion Plans
4 Deep Learning Breakdown Data by Type
4.1 Global Deep Learning Historic 麻豆原创 Size by Type (2019-2024)
4.2 Global Deep Learning Forecasted 麻豆原创 Size by Type (2025-2030)
5 Deep Learning Breakdown Data by Application
5.1 Global Deep Learning Historic 麻豆原创 Size by Application (2019-2024)
5.2 Global Deep Learning Forecasted 麻豆原创 Size by Application (2025-2030)
6 North America
6.1 North America Deep Learning 麻豆原创 Size (2019-2030)
6.2 North America Deep Learning 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
6.3 North America Deep Learning 麻豆原创 Size by Country (2019-2024)
6.4 North America Deep Learning 麻豆原创 Size by Country (2025-2030)
6.5 United States
6.6 Canada
7 Europe
7.1 Europe Deep Learning 麻豆原创 Size (2019-2030)
7.2 Europe Deep Learning 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
7.3 Europe Deep Learning 麻豆原创 Size by Country (2019-2024)
7.4 Europe Deep Learning 麻豆原创 Size by Country (2025-2030)
7.5 Germany
7.6 France
7.7 U.K.
7.8 Italy
7.9 Russia
7.10 Nordic Countries
8 Asia-Pacific
8.1 Asia-Pacific Deep Learning 麻豆原创 Size (2019-2030)
8.2 Asia-Pacific Deep Learning 麻豆原创 Growth Rate by Region: 2019 VS 2023 VS 2030
8.3 Asia-Pacific Deep Learning 麻豆原创 Size by Region (2019-2024)
8.4 Asia-Pacific Deep Learning 麻豆原创 Size by Region (2025-2030)
8.5 China
8.6 Japan
8.7 South Korea
8.8 Southeast Asia
8.9 India
8.10 Australia
9 Latin America
9.1 Latin America Deep Learning 麻豆原创 Size (2019-2030)
9.2 Latin America Deep Learning 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
9.3 Latin America Deep Learning 麻豆原创 Size by Country (2019-2024)
9.4 Latin America Deep Learning 麻豆原创 Size by Country (2025-2030)
9.5 Mexico
9.6 Brazil
10 Middle East & Africa
10.1 Middle East & Africa Deep Learning 麻豆原创 Size (2019-2030)
10.2 Middle East & Africa Deep Learning 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
10.3 Middle East & Africa Deep Learning 麻豆原创 Size by Country (2019-2024)
10.4 Middle East & Africa Deep Learning 麻豆原创 Size by Country (2025-2030)
10.5 Turkey
10.6 Saudi Arabia
10.7 UAE
11 Key Players Profiles
11.1 Amazon Web Services (AWS)
11.1.1 Amazon Web Services (AWS) Company Detail
11.1.2 Amazon Web Services (AWS) Business Overview
11.1.3 Amazon Web Services (AWS) Deep Learning Introduction
11.1.4 Amazon Web Services (AWS) Revenue in Deep Learning Business (2019-2024)
11.1.5 Amazon Web Services (AWS) Recent Development
11.2 Google
11.2.1 Google Company Detail
11.2.2 Google Business Overview
11.2.3 Google Deep Learning Introduction
11.2.4 Google Revenue in Deep Learning Business (2019-2024)
11.2.5 Google Recent Development
11.3 IBM
11.3.1 IBM Company Detail
11.3.2 IBM Business Overview
11.3.3 IBM Deep Learning Introduction
11.3.4 IBM Revenue in Deep Learning Business (2019-2024)
11.3.5 IBM Recent Development
11.4 Intel
11.4.1 Intel Company Detail
11.4.2 Intel Business Overview
11.4.3 Intel Deep Learning Introduction
11.4.4 Intel Revenue in Deep Learning Business (2019-2024)
11.4.5 Intel Recent Development
11.5 Micron Technology
11.5.1 Micron Technology Company Detail
11.5.2 Micron Technology Business Overview
11.5.3 Micron Technology Deep Learning Introduction
11.5.4 Micron Technology Revenue in Deep Learning Business (2019-2024)
11.5.5 Micron Technology Recent Development
11.6 Microsoft
11.6.1 Microsoft Company Detail
11.6.2 Microsoft Business Overview
11.6.3 Microsoft Deep Learning Introduction
11.6.4 Microsoft Revenue in Deep Learning Business (2019-2024)
11.6.5 Microsoft Recent Development
11.7 Nvidia
11.7.1 Nvidia Company Detail
11.7.2 Nvidia Business Overview
11.7.3 Nvidia Deep Learning Introduction
11.7.4 Nvidia Revenue in Deep Learning Business (2019-2024)
11.7.5 Nvidia Recent Development
11.8 Qualcomm
11.8.1 Qualcomm Company Detail
11.8.2 Qualcomm Business Overview
11.8.3 Qualcomm Deep Learning Introduction
11.8.4 Qualcomm Revenue in Deep Learning Business (2019-2024)
11.8.5 Qualcomm Recent Development
11.9 Samsung
11.9.1 Samsung Company Detail
11.9.2 Samsung Business Overview
11.9.3 Samsung Deep Learning Introduction
11.9.4 Samsung Revenue in Deep Learning Business (2019-2024)
11.9.5 Samsung Recent Development
11.10 Sensory Inc.
11.10.1 Sensory Inc. Company Detail
11.10.2 Sensory Inc. Business Overview
11.10.3 Sensory Inc. Deep Learning Introduction
11.10.4 Sensory Inc. Revenue in Deep Learning Business (2019-2024)
11.10.5 Sensory Inc. Recent Development
11.11 Skymind
11.11.1 Skymind Company Detail
11.11.2 Skymind Business Overview
11.11.3 Skymind Deep Learning Introduction
11.11.4 Skymind Revenue in Deep Learning Business (2019-2024)
11.11.5 Skymind Recent Development
11.12 Xilinx
11.12.1 Xilinx Company Detail
11.12.2 Xilinx Business Overview
11.12.3 Xilinx Deep Learning Introduction
11.12.4 Xilinx Revenue in Deep Learning Business (2019-2024)
11.12.5 Xilinx Recent Development
11.13 AMD
11.13.1 AMD Company Detail
11.13.2 AMD Business Overview
11.13.3 AMD Deep Learning Introduction
11.13.4 AMD Revenue in Deep Learning Business (2019-2024)
11.13.5 AMD Recent Development
11.14 General Vision
11.14.1 General Vision Company Detail
11.14.2 General Vision Business Overview
11.14.3 General Vision Deep Learning Introduction
11.14.4 General Vision Revenue in Deep Learning Business (2019-2024)
11.14.5 General Vision Recent Development
11.15 Graphcore
11.15.1 Graphcore Company Detail
11.15.2 Graphcore Business Overview
11.15.3 Graphcore Deep Learning Introduction
11.15.4 Graphcore Revenue in Deep Learning Business (2019-2024)
11.15.5 Graphcore Recent Development
11.16 Mellanox Technologies
11.16.1 Mellanox Technologies Company Detail
11.16.2 Mellanox Technologies Business Overview
11.16.3 Mellanox Technologies Deep Learning Introduction
11.16.4 Mellanox Technologies Revenue in Deep Learning Business (2019-2024)
11.16.5 Mellanox Technologies Recent Development
11.17 Huawei Technologies
11.17.1 Huawei Technologies Company Detail
11.17.2 Huawei Technologies Business Overview
11.17.3 Huawei Technologies Deep Learning Introduction
11.17.4 Huawei Technologies Revenue in Deep Learning Business (2019-2024)
11.17.5 Huawei Technologies Recent Development
11.18 Fujitsu
11.18.1 Fujitsu Company Detail
11.18.2 Fujitsu Business Overview
11.18.3 Fujitsu Deep Learning Introduction
11.18.4 Fujitsu Revenue in Deep Learning Business (2019-2024)
11.18.5 Fujitsu Recent Development
11.19 Baidu
11.19.1 Baidu Company Detail
11.19.2 Baidu Business Overview
11.19.3 Baidu Deep Learning Introduction
11.19.4 Baidu Revenue in Deep Learning Business (2019-2024)
11.19.5 Baidu Recent Development
11.20 Mythic
11.20.1 Mythic Company Detail
11.20.2 Mythic Business Overview
11.20.3 Mythic Deep Learning Introduction
11.20.4 Mythic Revenue in Deep Learning Business (2019-2024)
11.20.5 Mythic Recent Development
11.21 Adapteva
11.21.1 Adapteva Company Detail
11.21.2 Adapteva Business Overview
11.21.3 Adapteva Deep Learning Introduction
11.21.4 Adapteva Revenue in Deep Learning Business (2019-2024)
11.21.5 Adapteva Recent Development
11.22 Koniku
11.22.1 Koniku Company Detail
11.22.2 Koniku Business Overview
11.22.3 Koniku Deep Learning Introduction
11.22.4 Koniku Revenue in Deep Learning Business (2019-2024)
11.22.5 Koniku Recent Development
12 Analyst's Viewpoints/Conclusions
13 Appendix
13.1 Research Methodology
13.1.1 Methodology/Research Approach
13.1.2 Data Source
13.2 Disclaimer
13.3 Author Details
Amazon Web Services (AWS)
Google
IBM
Intel
Micron Technology
Microsoft
Nvidia
Qualcomm
Samsung
Sensory Inc.
Skymind
Xilinx
AMD
General Vision
Graphcore
Mellanox Technologies
Huawei Technologies
Fujitsu
Baidu
Mythic
Adapteva
Koniku
听
听
*If Applicable.
