Deep Learning Market Size, Share, & Trends Analysis Report By Solution (Hardware, Software), By Hardware, By Application (Image Recognition, Voice Recognition), By End-use, By Region, And Segment Forecasts, 2023 - 2030

Deep Learning Market Size, Share, & Trends Analysis Report By Solution (Hardware, Software), By Hardware, By Application (Image Recognition, Voice Recognition), By End-use, By Region, And Segment Forecasts, 2023 - 2030

Deep Learning Market Growth & Trends

The global deep learning market size is expected to reach USD 526.7 billion by 2030, expanding at a CAGR of 33.5% from 2023 to 2030, according to a new report by Grand View Research, Inc. Deep learning is expected to gain sustainable momentum in the coming years owing to its high computational ability and improved complex data-driven applications. The growing emphasis on big data analytics and the adoption of Artificial Intelligence (AI) in customer-centric services is expected to propel the growth of the deep learning industry over the forecast period.

AI has evolved rapidly in recent years, enabling machines to perform cognitive tasks effectively. The adoption of AI across various sectors has unlocked numerous potential opportunities for machine learning and deep learning applications. Furthermore, AI-as-a-service such as virtual assistants has allowed smaller organizations to implement AI algorithms required for deep learning applications without a large capital investment. Moreover, the availability of a large amount of data and the need for high computing power encourage SMEs and large enterprises to invest significantly in deep learning technology.

Deep learning allows the machine to solve complex problems even if the data is not well organized. A deep learning algorithm performs a task repeatedly, every time tweaking it to improve the outcomes. Thus, the more the task performed by the machines, the better will be the outcome. As a result, large amounts of unstructured data can be analyzed using deep learning algorithms and further deployed to obtain relevant insights for a more reliable decision-making process. For instance, organizations may use deep learning technology to unveil any data pointers between industry insights, social media conversation, and a stock price of a given organization.

Image and voice recognition are some of the leading applications in the deep learning industry. Several online and offline services such as Alexa virtual assistant by Amazon, Microsoft Cortana, and Siri use deep learning to acquire language skills while interacting with people. Facebook and Google have implemented deep learning technology for cognitive image analysis in their image classification application. It helps companies provide relevant results and automatic descriptions related to images.

Besides, deep learning algorithms can recreate a black-and-white image in color, offering impressive and accurate results in image colorization applications. For instance, In June 2019, Amazon introduced a new deep learning model called Alexa Conversations to create natural voice experiences on Alexa.

Deep learning offers lucrative investment opportunities for vendors due to the technology's high adoption rate. As a result, the companies consider product development as one of the strategic initiatives to capture the deep learning industry share. Recently, in February 2020, Google Inc. announced the launch of Reformer, an updated version of the transformer deep-learning model. In February 2020, Concentrix Corporation launched the deep learning algorithm tool for cybersecurity applications.

Deep Learning Market Report Highlights

  • The hardware segment is expected to witness the fastest growth over the forecast period owing to the growing demand for deep learning chipsets and reduced hardware costs
  • The Field Programmable Gate Array (FPGA) possesses high computational capability and has better power efficiency. As a result, the FPGA segment is expected to expand at the highest CAGR during the forecast period
  • Deep learning algorithms exhibit great potential in the automated extraction of complex data, thereby excelling in big data analytics application
  • Deep learning holds the potential to revolutionize the healthcare industry in the coming years by applying neural networks to analyze patient datasets to provide better outcomes
  • Several government initiatives related to digitalization and the growing adoption of next-generation technologies such as AI and machine learning in the APAC region exhibited the strong growth of deep learning technology
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Chapter 1 Market Segmentation and Scope
1.1 Market Segmentation and Scope
1.2 Market Definition
1.3 Information Procurement
1.3.1 Purchased Database
1.3.2 GVR'S Internal Database
1.3.3 Secondary Sources & Third-Party Perspectives
1.3.4 Primary Research
1.4 Information Analysis
1.4.1 Data Analysis Models
1.5 Market Formulation & Data Visualization
1.6 Data Validation & Publishing
Chapter 2 Executive Summary & Market Snapshot
2.1 Market Outlook
2.2 Segmental Outlook
2.3 Competitive Insights
Chapter 3 Deep Learnings Industry Outlook, Trends & Scope
3.1 Market Introduction
3.2 Deep Learning-Market Size and Growth Prospects
3.3 Deep Learning -Value Chain Analysis
3.4 Deep Learning -Market Dynamics
3.4.1 MARKET DRIVER ANALYSIS
3.4.1.1 Introduction of new hardware for deep learning applications
3.4.1.2 Improvement in deep learning algorithms
3.4.1.3 Increased penetration in big data analytics
3.4.2 MARKET RESTRAINT ANALYSIS
3.4.2.1 Scalability of deep learning models
3.4.2.2 Requirement of large training datasets for recognition
3.5 Deep Learning-Penetration & Growth Prospect Mapping
3.6 Business Environment Analysis Tools
3.6.1 PEST Analysis
3.6.2 Porter's Five Force Analysis
Chapter 4 Deep Learning Application Solution Outlook
4.1 Market Size Estimates & Forecasts and Trend Analysis, 2017 - 2030 (Revenue, USD Million)
4.2 Deep Learning Market: Solution Movement Analysis
4.2.1 Hardware
4.2.1.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
4.2.2 Software
4.2.2.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
4.2.3 Services
4.2.3.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
4.2.3.2 Installation Services
4.2.3.2.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
4.2.3.3 Integration Services
4.2.3.3.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
4.2.3.4 Maintenance & support services
4.2.3.4.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
Chapter 5 Deep Learning Hardware Outlook
5.1 Market Size Estimates & Forecasts and Trend Analysis, 2017 - 2030 (Revenue, USD Million)
5.2 Deep Learning Market: Hardware Movement Analysis
5.2.1 CPU
5.2.1.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
5.2.2 GPU
5.2.2.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
5.2.3 FPGA
5.2.3.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
5.2.4 ASIC
5.2.4.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
Chapter 6 Deep Learning Market Application Outlook
6.1 Market Size Estimates & Forecasts and Trend Analysis, 2017 - 2030 (Revenue, USD Million)
6.2 Deep Learning Market: Application Movement Analysis
6.2.1 Image recognition
6.2.1.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
6.2.2 Voice recognition
6.2.2.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
6.2.3 Video Surveillance & Diagnostics
6.2.3.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
6.2.4 Data Mining
6.2.4.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
Chapter 7 Deep Learning Market End-Use Outlook
7.1 Market Size Estimates & Forecasts and Trend Analysis, 2017 - 2030 (Revenue, USD Million)
7.2 Deep Learning Market: End-Use Movement Analysis
7.2.1 Automotive
7.2.1.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
7.2.2 Aerospace & Defense
7.2.2.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
7.2.3 Healthcare
7.2.3.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
7.2.4 Retail
7.2.4.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
7.2.5 Others
7.2.5.1 Market estimates and forecast by region, 2017 - 2030 (USD Million)
Chapter 8 Regional Estimates & Trend Analysis
8.1 Market Size Estimates & Forecasts and Trend Analysis, 2017 - 2030 (Revenue, USD Million)
8.2 Deep Learning Market Share by Region, 2022 & 2030
8.3 North America
8.3.1 Market estimates and forecast by solution, 2017 - 2030 (USD Million)
8.3.2 Market estimates and forecast by hardware, 2017 - 2030 (USD Million)
8.3.3 Market estimates and forecast by service, 2017 - 2030 (USD Million)
8.3.4 Market estimates and forecast by application, 2017 - 2030 (USD Million)
8.3.5 Market estimates and forecast by end-use, 2017 - 2030 (USD Million)
8.3.6 U.S.
8.3.6.1 Market estimates and forecast by solution, 2017 - 2030 (USD Million)
8.3.6.2 Market estimates and forecast by hardware, 2017 - 2030 (USD Million)
8.3.6.3 Market estimates and forecast by service, 2017 - 2030 (USD Million)
8.3.6.4 Market estimates and forecast by application, 2017 - 2030 (USD Million)
8.3.6.5 Market estimates and forecast by end-use, 2017 - 2030 (USD Million)
8.3.7 Canada
8.3.7.1 Market estimates and forecast by solution, 2017 - 2030 (USD Million)
8.3.7.2 Market estimates and forecast by hardware, 2017 - 2030 (USD Million)
8.3.7.3 Market estimates and forecast by service, 2017 - 2030 (USD Million)
8.3.7.4 Market estimates and forecast by application, 2017 - 2030 (USD Million)
8.3.7.5 Market estimates and forecast by end-use, 2017 - 2030 (USD Million)
8.3.8 Mexico
8.3.8.1 Market estimates and forecast by solution, 2017 - 2030 (USD Million)
8.3.8.2 Market estimates and forecast by hardware, 2017 - 2030 (USD Million)
8.3.8.3 Market estimates and forecast by service, 2017 - 2030 (USD Million)
8.3.8.4 Market estimates and forecast by application, 2017 - 2030 (USD Million)
8.3.8.5 Market estimates and forecast by end-use, 2017 - 2030 (USD Million)
8.4 Europe
8.4.1 Market estimates and forecast by solution, 2017 - 2030 (USD Million)
8.4.2 Market estimates and forecast by hardware, 2017 - 2030 (USD Million)
8.4.3 Market estimates and forecast by service, 2017 - 2030 (USD Million)
8.4.4 Market estimates and forecast by application, 2017 - 2030 (USD Million)
8.4.5 Market estimates and forecast by end-use, 2017 - 2030 (USD Million)
8.4.6 Germany
8.4.6.1 Market estimates and forecast by solution, 2017 - 2030 (USD Million)
8.4.6.2 Market estimates and forecast by hardware, 2017 - 2030 (USD Million)
8.4.6.3 Market estimates and forecast by service, 2017 - 2030 (USD Million)
8.4.6.4 Market estimates and forecast by application, 2017 - 2030 (USD Million)
8.4.6.5 Market estimates and forecast by end-use, 2017 - 2030 (USD Million)
8.4.7 U.K.
8.4.7.1 Market estimates and forecast by solution, 2017 - 2030 (USD Million)
8.4.7.2 Market estimates and forecast by hardware, 2017 - 2030 (USD Million)
8.4.7.3 Market estimates and forecast by service, 2017 - 2030 (USD Million)
8.4.7.4 Market estimates and forecast by application, 2017 - 2030 (USD Million)
8.4.7.5 Market estimates and forecast by end-use, 2017 - 2030 (USD Million)
8.5 Asia Pacific
8.5.1 Market estimates and forecast by solution, 2017 - 2030 (USD Million)
8.5.2 Market estimates and forecast by hardware, 2017 - 2030 (USD Million)
8.5.3 Market estimates and forecast by service, 2017 - 2030 (USD Million)
8.5.4 Market estimates and forecast by application, 2017 - 2030 (USD Million)
8.5.5 Market estimates and forecast by end-use, 2017 - 2030 (USD Million)
8.5.6 China
8.5.6.1 Market estimates and forecast by solution, 2017 - 2030 (USD Million)
8.5.6.2 Market estimates and forecast by hardware, 2017 - 2030 (USD Million)
8.5.6.3 Market estimates and forecast by service, 2017 - 2030 (USD Million)
8.5.6.4 Market estimates and forecast by application, 2017 - 2030 (USD Million)
8.5.6.5 Market estimates and forecast by end-use, 2017 - 2030 (USD Million)
8.5.7 India
8.5.7.1 Market estimates and forecast by solution, 2017 - 2030 (USD Million)
8.5.7.2 Market estimates and forecast by hardware, 2017 - 2030 (USD Million)
8.5.7.3 Market estimates and forecast by service, 2017 - 2030 (USD Million)
8.5.7.4 Market estimates and forecast by application, 2017 - 2030 (USD Million)
8.5.7.5 Market estimates and forecast by end-use, 2017 - 2030 (USD Million)
8.5.8 Japan
8.5.8.1 Market estimates and forecast by solution, 2017 - 2030 (USD Million)
8.5.8.2 Market estimates and forecast by hardware, 2017 - 2030 (USD Million)
8.5.8.3 Market estimates and forecast by service, 2017 - 2030 (USD Million)
8.5.8.4 Market estimates and forecast by application, 2017 - 2030 (USD Million)
8.5.8.5 Market estimates and forecast by end-use, 2017 - 2030 (USD Million)
8.6 South America
8.6.1 Market estimates and forecast by solution, 2017 - 2030 (USD Million)
8.6.2 Market estimates and forecast by hardware, 2017 - 2030 (USD Million)
8.6.3 Market estimates and forecast by service, 2017 - 2030 (USD Million)
8.6.4 Market estimates and forecast by application, 2017 - 2030 (USD Million)
8.6.5 Market estimates and forecast by end-use, 2017 - 2030 (USD Million)
8.6.6 Brazil
8.6.6.1 Market estimates and forecast by solution, 2017 - 2030 (USD Million)
8.6.6.2 Market estimates and forecast by hardware, 2017 - 2030 (USD Million)
8.6.6.3 Market estimates and forecast by service, 2017 - 2030 (USD Million)
8.6.6.4 Market estimates and forecast by application, 2017 - 2030 (USD Million)
8.6.6.5 Market estimates and forecast by end-use, 2017 - 2030 (USD Million)
8.7 MEA
8.7.1 Market estimates and forecast by solution, 2017 - 2030 (USD Million)
8.7.2 Market estimates and forecast by hardware, 2017 - 2030 (USD Million)
8.7.3 Market estimates and forecast by service, 2017 - 2030 (USD Million)
8.7.4 Market estimates and forecast by application, 2017 - 2030 (USD Million)
8.7.5 Market estimates and forecast by end-use, 2017 - 2030 (USD Million)
Chapter 9 Competitive Analysis
9.1 Key Global Players, Recent Developments & Their Impact on the Industry
9.2 Key Company Categorization (Key innovators, Market leaders, and Emerging players)
9.3 Key Company Analysis, 2022
Chapter 10 Competitive Landscape
10.1 Advanced Micro Devices, Inc.
10.1.1 Company overview
10.1.2 Financial performance
10.1.3 Product benchmarking
10.1.4 Recent developments
10.2 ARM Ltd.
10.2.1 Company Overview
10.2.2 Financial performance
10.2.3 Product benchmarking
10.2.4 Recent developments
10.3 Clarifai, Inc.
10.3.1 Company overview
10.3.2 Financial performance
10.3.3 Product benchmarking
10.3.4 Recent developments
10.4 Entilic
10.4.1 Company overview
10.4.2 Financial performance
10.4.3 Product benchmarking
10.4.4 Recent developments
10.5 Google, Inc.
10.5.1 Company overview
10.5.2 Financial performance
10.5.3 Product benchmarking
10.5.4 Recent developments
10.6 HyperVerge
10.6.1 Company overview
10.6.2 Financial performance
10.6.3 Product benchmarking
10.6.4 Recent developments
10.7 IBM Corporation
10.7.1 Company overview
10.7.2 Financial performance
10.7.3 Product benchmarking
10.7.4 Recent developments
10.8 Intel Corporation
10.8.1 Company overview
10.8.2 Financial performance
10.8.2 Product benchmarking
10.8.3 Recent developments
10.9 Microsoft Corporation
10.9.1 Company overview
10.9.2 Financial performance
10.9.3 Product benchmarking
10.9.4 Recent developments
10.10 NVIDIA Corporation
10.10.1 Company overview
10.10.2 Financial performance
10.10.3 Product benchmarking
10.10.4 Recent developments

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