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Global Deep Learning Market to reach USD 374.81 billion by the end of 2029.

Global Deep Learning Market Size study & Forecast, by Solution(Hardware, Software and Services), by Hardware (Central Processing Unit (CPU), Graphics Processing Unit (GPU), Field Programmable Gate Array (FPGA), Application-Specific Integration Circuit (ASIC))by Application (Image recognition, Voice recognition, Video surveillance & diagnostics and Data mining), by End Use (Automotive, Aerospace & Defense, Healthcare, Retail and Others) and Regional Analysis, 2022-2029

Product Code: ICTNGT-89596673
Publish Date: 2-05-2023
Page: 200

Global Deep Learning Market is valued approximately at USD 37.15billion in 2021 and is anticipated to grow with a healthy growth rate of more than 33.5% over the forecast period 2022-2029. A sequence of computer instructions or algorithms known as “deep learning” are based on the structure and operation of the brain. It is a branch of machine learning. Deep learning is a method for teaching computers to learn by doing. Deep learning is frequently referred to as artificial neural networks or deep neural networks. Statistics and predictive modelling are also important components of data science, which also includes deep learning. The Deep Learning market is expanding because of factors such as increased penetration in big data analytics, improvement in deep learning algorithms and rising investment for the development of AI and ML However, requirement of large training datasets for recognition and high capital investment may halt market growth.

According to Statista, in year 2018 Volume of data/information created, captured, copied, and consumed worldwide stood at 33 zettabytes which increased to 79 zettabytes in year 2021 and it is projected to reach at 181 zettabytes by year 2025. Thus, rise in generation of data volume across the world is fostering market growth. Thus rising data volume generation across the world is catering the market growth. Along with these, the market is growing rapidly due to the widespread adoption of connected devices in educational institutions and government measures to enhance industry vertical digitalization. For instance, the Australian Catholic University (ACU) created a data lake in July 2020 using its newly combined data environment to offer a unified view of student progress. In order to assist students who might require further support or who run the risk of quitting the university altogether, it makes use of Microsoft’s Power BI and Azure data platform. As a result, this is seen as a significant component promoting market expansion. In addition, increasing adoption of cloud-based technology across several industries. However, the high cost of Deep Learning stifles market growth throughout the forecast period of 2022-2029.

The key regions considered for the Global Deep Learning Market study includes Asia Pacific, North America, Europe, Latin America, and Rest of the World. North America dominated the market in terms of revenue, owing to rising investment in artificial intelligence, rise in adoption of image and pattern recognition technology. Whereas, Europe is expected to grow with a highest CAGR during the forecast period, owing to factors such as rising government initiatives to adopt artificial intelligence and machine learning in various end use industry.

Major market player included in this report are:
Advanced Micro Devices, Inc.
ARM Ltd.
Clarifai, Inc.
Entilic
Google, Inc.
HyperVerge
IBM Corporation
Intel Corporation
Microsoft Corporation
NVIDIA Corporation

Recent Developments in the Market:
Ø In June 2020, Facebook AI Research announced the launch of TransCoder. It is a system which utilizes unsupervised deep-learning in conversion of code from one programming language to the another.
Ø In May 2020, IBM announced that it would use a variety of artificial intelligence (AI) technologies to automate the management of IT operations and modernise applications. It employs machine learning and deep learning algorithms to time series data, semi-structured logs, structured data, and unstructured data spanning IT issues and human dialogues to trace the history of a problem.

Global Deep Learning Market Report Scope:
Historical Data 2019-2020-2021
Base Year for Estimation 2021
Forecast period 2022-2029
Report Coverage Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
Segments Covered Solution, Hardware, Application, End use, Region
Regional Scope North America; Europe; Asia Pacific; Latin America; Rest of the World
Customization Scope Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and Solution offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Solution:
Hardware
Software
Services

By Hardware:
Central Processing Unit (CPU)
Graphics Processing Unit (GPU)
Field Programmable Gate Array (FPGA)
Application-Specific Integration Circuit (ASIC)
By Application:
Image recognition
Voice recognition
Video surveillance & diagnostics
Data mining
By End Use:
Automotive
Aerospace & Defense
Healthcare
Retail
Others
By Region:
North America
U.S.
Canada
Europe
UK
Germany
France
Spain
Italy
ROE
Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC
Latin America
Brazil
Mexico
RoLA
Rest of the World

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2019-2029 (USD Billion)
1.2.1. Deep Learning Market, by Region, 2019-2029 (USD Billion)
1.2.2. Deep Learning Market, by Solution, 2019-2029 (USD Billion)
1.2.3. Deep Learning Market, by Hardware, 2019-2029 (USD Billion)
1.2.4. Deep Learning Market, by Application, 2019-2029 (USD Billion)
1.2.5. Deep Learning Market, by End Use, 2019-2029 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Deep Learning Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Deep Learning Market Dynamics
3.1. Deep Learning Market Impact Analysis (2019-2029)
3.1.1. Market Drivers
3.1.1.1. Increased penetration in big data analytics
3.1.1.2. Improvement in deep learning algorithms
3.1.1.3. Rising investment for the development of AI and ML
3.1.2. Market Challenges
3.1.2.1. Requirement of large training datasets for recognition
3.1.2.2. High Capital Investment
3.1.3. Market Opportunities
3.1.3.1. Increasing adoption of cloud-based technology across several industries
Chapter 4. Global Deep Learning Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.2. Futuristic Approach to Porter’s 5 Force Model (2019-2029)
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. Industry Experts Prospective
4.7. Analyst Recommendation & Conclusion
Chapter 5. Risk Assessment: COVID-19 Impact
5.1. Assessment of the overall impact of COVID-19 on the industry
5.2. Pre COVID-19 and post COVID-19 Market scenario
Chapter 6. Global Deep Learning Market, by Solution
6.1. Market Snapshot
6.2. Global Deep Learning Market by Solution, Performance – Potential Analysis
6.3. Global Deep Learning Market Estimates & Forecasts by Solution 2019-2029 (USD Billion)
6.4. Deep Learning Market, Sub Segment Analysis
6.4.1. Hardware
6.4.2. Software
6.4.3. Services
Chapter 7. Global Deep Learning Market, by Hardware
7.1. Market Snapshot
7.2. Global Deep Learning Market by Hardware, Performance – Potential Analysis
7.3. Global Deep Learning Market Estimates & Forecasts by Hardware 2019-2029 (USD Billion)
7.4. Deep Learning Market, Sub Segment Analysis
7.4.1. Central Processing Unit (CPU)
7.4.2. Graphics Processing Unit (GPU)
7.4.3. Field Programmable Gate Array (FPGA)
7.4.4. Application-Specific Integration Circuit (ASIC)
Chapter 8. Global Deep Learning Market, by Application
8.1. Market Snapshot
8.2. Global Deep Learning Market by Application, Performance – Potential Analysis
8.3. Global Deep Learning Market Estimates & Forecasts by Application 2019-2029 (USD Billion)
8.4. Deep Learning Market, Sub Segment Analysis
8.4.1. Image recognition
8.4.2. Voice recognition
8.4.3. Video surveillance & diagnostics
8.4.4. Data mining
Chapter 9. Global Deep Learning Market, by End Use
9.1. Market Snapshot
9.2. Global Deep Learning Market by End Use, Performance – Potential Analysis
9.3. Global Deep Learning Market Estimates & Forecasts by End Use 2019-2029 (USD Billion)
9.4. Deep Learning Market, Sub Segment Analysis
9.4.1. Automotive
9.4.2. Aerospace & Defense
9.4.3. Healthcare
9.4.4. Retail
9.4.5. Others
Chapter 10. Global Deep Learning Market, Regional Analysis
10.1. Deep Learning Market, Regional Market Snapshot
10.2. North America Deep Learning Market
10.2.1. U.S. Deep Learning Market
10.2.1.1. Solution breakdown estimates & forecasts, 2019-2029
10.2.1.2. Hardware breakdown estimates & forecasts, 2019-2029
10.2.1.3. Application breakdown estimates & forecasts, 2019-2029
10.2.1.4. End Use breakdown estimates & forecasts, 2019-2029
10.2.2. Canada Deep Learning Market
10.3. Europe Deep Learning Market Snapshot
10.3.1. U.K. Deep Learning Market
10.3.2. Germany Deep Learning Market
10.3.3. France Deep Learning Market
10.3.4. Spain Deep Learning Market
10.3.5. Italy Deep Learning Market
10.3.6. Rest of Europe Deep Learning Market
10.4. Asia-Pacific Deep Learning Market Snapshot
10.4.1. China Deep Learning Market
10.4.2. India Deep Learning Market
10.4.3. Japan Deep Learning Market
10.4.4. Australia Deep Learning Market
10.4.5. South Korea Deep Learning Market
10.4.6. Rest of Asia Pacific Deep Learning Market
10.5. Latin America Deep Learning Market Snapshot
10.5.1. Brazil Deep Learning Market
10.5.2. Mexico Deep Learning Market
10.5.3. Rest of Latin America Deep Learning Market
10.6. Rest of The World Deep Learning Market

Chapter 11. Competitive Intelligence
11.1. Top Market Strategies
11.2. Company Profiles
11.2.1. Advanced Micro Devices, Inc.
11.2.1.1. Key Information
11.2.1.2. Overview
11.2.1.3. Financial (Subject to Data Availability)
11.2.1.4. Product Summary
11.2.1.5. Recent Developments
11.2.2. ARM Ltd.
11.2.3. Clarifai, Inc.
11.2.4. Entilic
11.2.5. Google, Inc.
11.2.6. HyperVerge
11.2.7. IBM Corporation
11.2.8. Intel Corporation
11.2.9. Microsoft Corporation
11.2.10. NVIDIA Corporation
Chapter 12. Research Process
12.1. Research Process
12.1.1. Data Mining
12.1.2. Analysis
12.1.3. Market Estimation
12.1.4. Validation
12.1.5. Publishing
12.2. Research Attributes
12.3. Research Assumption

At Bizwit Research and Consultancy, we employ a thorough and iterative research methodology with the goal of minimizing discrepancies, ensuring the provision of highly accurate estimates and predictions over the forecast period. Our approach involves a combination of bottom-up and top-down strategies to effectively segment and estimate quantitative aspects of the market, utilizing our proprietary data & AI tools. Our Proprietary Tools allow us for the creation of customized models specific to the research objectives. This enables us to develop tailored statistical models and forecasting algorithms to estimate market trends, future growth, or consumer behavior. The customization enhances the accuracy and relevance of the research findings.
We are dedicated to clearly communicating the purpose and objectives of each research project in the final deliverables. Our process begins by identifying the specific problem or challenge our client wishes to address, and from there, we establish precise research questions that need to be answered. To gain a comprehensive understanding of the subject matter and identify the most relevant trends and best practices, we conduct an extensive review of existing literature, industry reports, case studies, and pertinent academic research.
Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
After conducting the research, our experts analyze the findings in relation to the research objectives and the specific needs of the client. They generate valuable insights and recommendations that directly address the client’s business challenges. These insights are carefully connected to the research findings to provide a comprehensive understanding.
Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

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