Global Deep Learning Market: Industry Analysis and Forecast (2021-2027) by Component, Application, Architecture Industry, End-Use Industry, and Region

Global Deep Learning Market size was valued at US$ 6.05 Bn in 2020 and the total revenue is expected to grow at 41.5 % through 2021 to 2027, reaching nearly US$ 68.71 Bn.

Global Deep Learning Market Characteristics:

IBM is a worldwide technology firm that offers hardware, software, cloud-based services, and cognitive technologies. Charles Ranlett Flint established the Computing-Tabulating-Recording Company in 1911, succeeding the amalgamation of four enterprises in New York State. IBM had revenue of USD 73.62 Billion in 2020. The Americas area is IBM's largest source of revenue, accounting since over US $ 34 billion - almost a half of the company's total sales. While, the Europe and Asia Pacific revenues are US $ 23.64 and US $ 15.86 in 2020. Global Deep Learning Market To know about the Research Methodology :- Request Free Sample Report 2020 is considered as a base year to forecast the market from 2021 to 2027. 2020’s market size is estimated on real numbers and outputs of the key players and major players across the globe. Past five years trends are considered while forecasting the market through 2027. 2020 is a year of exception and analyzed specially with the impact of lockdown by region.

Global Deep Learning Market Overview:

Deep learning, also known as deep structured learning, is a subset of machine learning that analyses data using layered computational models. It is a critical component of data science, which collects, analyses, and interprets enormous amounts of data using statistics and prescriptive analytics. It also includes the use of artificial intelligence (AI) to simulate how the human brain works when processing data, generating trends, and making choices. This technology is frequently used in face recognition software, natural language processing (NLP) and speech synthesis software, self-driving vehicles, and language translation services, and it has a lot of functions in retail, medical, automotive, agriculture, security, and industrial.

Global Deep Learning Market Dynamics:

The growing use of cloud-based services, as well as the large-scale creation of unstructured information, has increased the demand for deep learning solutions. Furthermore, the growing applications of deep learning in recent times for image/speech recognition, data analysis, and language interpretations, as well as the increasing number of robotic systems, such as Sophia, produced by Hanson Robotics, are some of the key drivers of the deep learning industry. Key market participants' increasing efforts in developing machine learning and deep learning technologies in the area are estimated to boost market growth. Additionally, the rapid expansion in the volume of data generated in various end-use industries is predicted to drive industry growth. Also, the growing demand for human-machine interaction is opening up new opportunities for software vendors to provide upgraded services and competences. Besides that, the prevalence of deep learning integration with big data analytics, as well as the growing need to enhance computational power and lower hardware costs due to deep learning algorithms' ability to run or execute faster on a GPU as opposed to a CPU, is resulting in widespread acceptance of deep learning technologies across industries, which is estimated to increase global growth. There are various constraints and hurdles that will impede overall market growth. The paucity of standards and protocols, as well as a dearth of technical competence in deep learning, are constraining market expansion. Furthermore, sophisticated integrated systems, as well as the merging of deep learning solutions and software into legacy infrastructure, are arduous tasks that limit growth. Additionally, growing hardware complexity due to complicated algorithms, a lack of management support and multiplexing, and the implementation of DL for applications such as NLP in regional languages are possible barriers impeding the global deep learning market's overall growth. Nonetheless, technological developments, the availability of restricted structured information to raise demand for deep learning solutions, aggregate spending in the healthcare, travel, tourist, and hospitality industries, and possible opportunities in emerging economies provide attractive growth potential.

COVID-19 Analysis:

The COVID-19 epidemic has aided in the re-emergence of highly desirable technology such as artificial intelligence, learning techniques, machine learning, DevOps, and big data. Deep learning technology was already having a significant impact on mobility, healthcare, banking, and industrial prior to the epidemic. Industrial organisations were encouraged to adopt new automation and control technologies to counter increased wages and maximize productivity as a result of pandemic-related logistics challenges. As a result, the worldwide deep learning market started to acquire steam, with continuous increases in automation spending.

Global Deep Learning Market Segment Analysis:

The global Deep Learning Market is segmented by Component, Application, Architecture Industry, and End-Use Industry. Based on the Component, the global Deep Learning market is segmented into Hardware and Software. The Software segment is expected to grow at a higher CAGR of xx% in the global deep learning during the forecast period. The growing use of software applications in a variety of applications, including as smartphone aides, ATMs that read checks, voice and picture recognition system on social networks, and software that displays adverts on a variety of websites, is propelling the market for machine learning technology. Depending on the design, most organisations that construct and design systems and accompanying software provide both online and offline support. Several firms offer implementation, training, and support for these systems, as well as online help and post-maintenance of software and essential services. Global Deep Learning Market Based on the Application, the global Deep Learning market is segmented into Speech Recognition, Image Recognition, Data Mining, Drug Discovery, Driver Assistance, and Others. The image recognition segment held the largest market share of xx% in 2020. The factors that might be connected to rising need for pattern recognition, optical character recognition, code recognition, facial recognition, machine vision, and image analysis are driving the image recognition segment's demand. Global Deep Learning Market Based on the Architecture Industry, the global Deep Learning market is segmented into RNN, CNN, DBN, DSN, and GRU. The recurrent neural networks (RNN) segment held the largest market share of xx% in 2020 and is expected to remain dominant over the forecast. Recurrent Neural Networks (RNN) are a powerful and vigorous type of neural networks and belong to the most capable algorithms at the moment, as they are the only ones with an internal memory. Due to their internal memory, RNN’s are able to remember significant things about the input they received, which allows them to be very accurate in predicting what’s coming next. It is possible to group clustered data into smaller sets for easy processing with the help of deep learning. Deep learning is having multiple applications in computer and bio medical sciences among others. It is possible to read the complex DNA information faster, it is also possible to calculate the future variations in data clusters by analysing the previous available data. Deep learning have a huge potential in many markets. It can help to create better Artificial Intelligence systems that can modernize the industries as we know it. Many AI systems are already in expansion which use deep learning to full potential. Global Deep Learning Market

Global Deep Learning Market Regional Insights:

North America dominates the global Deep Learning market during the forecast period 2021-2027. North America is expected to hold the largest market share 42.4% by 2027. This is due to growing funding in artificial intelligence and neural networks. The region's widespread use of image and monitoring purposes is estimated to generate new growth prospects over the forecast period. Also, the region is one of the pioneers of modern technologies, allowing firms to accelerate the adoption of deep learning ability. Asia Pacific is expected to grow at a higher CAGR of 37.1% in the global deep learning market during the forecast period 2021-2027. Deep learning is becoming more popular as this technology is employed in more than only electrical items like smartphones, tablets, and PCs, and yet also healthcare and automotive products. The rapid economic development of key nations such as China and India is important to encourage the growth of the Asia Pacific deep learning market. Global Deep Learning MarketThe objective of the report is to present a comprehensive analysis of the global Deep Learning Market to the stakeholders in the industry. The past and current status of the industry with the forecasted market size and trends are presented in the report with the analysis of complicated data in simple language. The report covers all the aspects of the industry with a dedicated study of key players that include market leaders, followers, and new entrants. PORTER, PESTEL analysis with the potential impact of micro-economic factors of the market have been presented in the report. External as well as internal factors that are supposed to affect the business positively or negatively have been analyzed, which will give a clear futuristic view of the industry to the decision-makers. The reports also helps in understanding the global Deep Learning Market dynamic, structure by analyzing the market segments and project the global Deep Learning Market size. Clear representation of competitive analysis of key players by product, price, financial position, product portfolio, growth strategies, and regional presence in the global Deep Learning Market make the report investor’s guide.

Global Deep Learning Market Scope: Inquire before buying

Global Deep Learning Market

Global Deep Learning Market, by Region

• North America • Europe • Asia Pacific • South America • Middle East and Africa

Global Deep Learning Market Key Players

Advanced Micro Devices, Inc. • Arm Ltd. • Baidu Inc. • Clairifai Inc. • Enlitic • General Vision Inc. • Google Inc. • Hewlett Packard • IBM Corporation Intel Corporation • Microsoft Corporation • Nvidia Corporation • Qualcomm Technologies Inc. • Sensory Inc. • Skymind • Alphabet Inc. • Micron Technology Inc. • Amazon Web Services • Graphcore • Xilinx • Sensory Inc. • Mellanox Technologies

Frequently Asked Questions:

1] What segments are covered in Deep Learning Market report? Ans. The segments covered in Deep Learning Market report are based on Component, Application, Architecture Industry, and End-Use Industry. 2] Which region is expected to hold the highest share in the global Deep Learning Market? Ans. North America is expected to hold the highest share in the global Deep Learning Market. 3] What is the market size of global Deep Learning Market by 2027? Ans. The market size of global Deep Learning Market by 2027 is US $ 68.71 Bn. 4] Who are the top key players in the global Deep Learning Market? Ans. Advanced Micro Devices, Inc., Arm Ltd., Baidu Inc., Clairifai Inc., Enlitic, General Vision Inc., Google Inc., are the top key players in the global Deep Learning Market. 5] What was the market size of global Deep Learning Market in 2020? Ans. The market size of global Deep Learning Market in 2020 was US $ 6.05 Bn. 6] By Component, What segments are covered in Deep Learning Market report? Ans. The segments covered in Deep Learning Market report by Component are Hardware and Software.

Global Deep Learning Market

1. Preface 1.1. Report Scope and Market Segmentation 1.2. Research Highlights 1.3. Research Objectives 2. Assumptions and Research Methodology 2.1. Report Assumptions 2.2. Abbreviations 2.3. Research Methodology 2.3.1. Secondary Research 2.3.1.1. Secondary data 2.3.1.2. Secondary Sources 2.3.2. Primary Research 2.3.2.1. Data from Primary Sources 2.3.2.2. Breakdown of Primary Sources 3. Executive Summary: Global Deep Learning Market, by Market Value (US$ Bn) 4. Market Overview 4.1. Introduction 4.2. Market Indicator 4.2.1. Drivers 4.2.2. Restraints 4.2.3. Opportunities 4.2.4. Challenges 4.3. Porter’s Analysis 4.4. Value Chain Analysis 4.5. Market Risk Analysis 4.6. SWOT Analysis 4.7.  Global Deep Learning Market Industry Trend and Emerging Technologies 5. Supply Side and Demand Side Indicators 6. Global Deep Learning Market Analysis and Forecast 6.1. Global Deep Learning Market Size & Y-o-Y Growth Analysis 6.1.1. North America 6.1.2. Europe 6.1.3. Asia Pacific 6.1.4. Middle East & Africa 6.1.5. South America 7. Global Deep Learning Market Analysis and Forecast, by Component 7.1. Introduction and Definition 7.2. Key Findings 7.3. Global Deep Learning Market Value Share Analysis, by Component 7.4. Global Deep Learning Market Size (US$ Bn) Forecast, by Component 7.5. Global Deep Learning Market Analysis, by Component 7.6. Global Deep Learning Market Attractiveness Analysis, by Component 8. Global Deep Learning Market Analysis and Forecast, by Application 8.1. Introduction and Definition 8.2. Key Findings 8.3. Global Deep Learning Market Value Share Analysis, by Application 8.4. Global Deep Learning Market Size (US$ Bn) Forecast, by Application 8.5. Global Deep Learning Market Analysis, by Application 8.6. Global Deep Learning Market Attractiveness Analysis, by Application 9. Global Deep Learning Market Analysis and Forecast, by Architecture Industry 9.1. Introduction and Definition 9.2. Key Findings 9.3. Global Deep Learning Market Value Share Analysis, by Architecture Industry 9.4. Global Deep Learning Market Size (US$ Bn) Forecast, by Architecture Industry 9.5. Global Deep Learning Market Analysis, by Architecture Industry 9.6. Global Deep Learning Market Attractiveness Analysis, by Architecture Industry 10. Global Deep Learning Market Analysis and Forecast, by End Use Industry 10.1. Introduction and Definition 10.2. Key Findings 10.3. Global Deep Learning Market Value Share Analysis, by End Use Industry 10.4. Global Deep Learning Market Size (US$ Bn) Forecast, by End Use Industry 10.5. Global Deep Learning Market Analysis, by End Use Industry 10.6. Global Deep Learning Market Attractiveness Analysis, by End Use Industry 11. Global Deep Learning Market Analysis, by Region 11.1. Global Deep Learning Market Value Share Analysis, by Region 11.2. Global Deep Learning Market Size (US$ Bn) Forecast, by Region 11.3. Global Deep Learning Market Attractiveness Analysis, by Region 12. North America Deep Learning Market Analysis 12.1. Key Findings 12.2. North America Deep Learning Market Overview 12.3. North America Deep Learning Market Value Share Analysis, by Component 12.4. North America Deep Learning Market Forecast, by Component 12.4.1. Hardware 12.4.2. Software 12.5. North America Deep Learning Market Value Share Analysis, by Application 12.6. North America Deep Learning Market Forecast, by Application 12.6.1. Speech Recognition 12.6.2. Image Recognition 12.6.3. Data Mining 12.6.4. Drug Discovery 12.6.5. Driver Assistance 12.6.6. Others 12.7. North America Deep Learning Market Value Share Analysis, by Architecture Industry 12.8. North America Deep Learning Market Forecast, by Architecture Industry 12.8.1. RNN 12.8.2. CNN 12.8.3. DBN 12.8.4. DSN 12.8.5. GRU 12.9. North America Deep Learning Market Value Share Analysis, by End Use Industry 12.10. North America Deep Learning Market Forecast, by End Use Industry 12.10.1. Healthcare 12.10.2. Automotive 12.10.3. Media & Entertainment 12.10.4. BFSI 12.10.5. Other 12.11. North America Deep Learning Market Value Share Analysis, by Country 12.12. North America Deep Learning Market Forecast, by Country 12.12.1. U.S. 12.12.2. Canada 12.13. North America Deep Learning Market Analysis, by Country 12.14. U.S. Deep Learning Market Forecast, by Component 12.14.1. Hardware 12.14.2. Software 12.15. U.S. Deep Learning Market Forecast, by Application 12.15.1. Speech Recognition 12.15.2. Image Recognition 12.15.3. Data Mining 12.15.4. Drug Discovery 12.15.5. Driver Assistance 12.15.6. Others 12.16. U.S. Deep Learning Market Forecast, by Architecture Industry 12.16.1. RNN 12.16.2. CNN 12.16.3. DBN 12.16.4. DSN 12.16.5. GRU 12.17. U.S. Deep Learning Market Forecast, by End Use Industry 12.17.1. Healthcare 12.17.2. Automotive 12.17.3. Media & Entertainment 12.17.4. BFSI 12.17.5. Other 12.18. Canada Deep Learning Market Forecast, by Component 12.18.1. Hardware 12.18.2. Software 12.19. Canada Deep Learning Market Forecast, by Application 12.19.1. Speech Recognition 12.19.2. Image Recognition 12.19.3. Data Mining 12.19.4. Drug Discovery 12.19.5. Driver Assistance 12.19.6. Others 12.20. Canada Deep Learning Market Forecast, by Architecture Industry 12.20.1. RNN 12.20.2. CNN 12.20.3. DBN 12.20.4. DSN 12.20.5. GRU 12.21. Canada Deep Learning Market Forecast, by End Use Industry 12.21.1. Healthcare 12.21.2. Automotive 12.21.3. Media & Entertainment 12.21.4. BFSI 12.21.5. Other 12.22. North America Deep Learning Market Attractiveness Analysis 12.22.1. By Component 12.22.2. By Application 12.22.3. By Architecture Industry 12.22.4. By End Use Industry 12.23. PEST Analysis 12.24. Key Trends 12.25. Key Development 13. Europe Deep Learning Market Analysis 13.1. Key Findings 13.2. Europe Deep Learning Market Overview 13.3. Europe Deep Learning Market Value Share Analysis, by Component 13.4. Europe Deep Learning Market Forecast, by Component 13.4.1. Hardware 13.4.2. Software 13.5. Europe Deep Learning Market Value Share Analysis, by Application 13.6. Europe Deep Learning Market Forecast, by Application 13.6.1. Speech Recognition 13.6.2. Image Recognition 13.6.3. Data Mining 13.6.4. Drug Discovery 13.6.5. Driver Assistance 13.6.6. Others 13.7. Europe Deep Learning Market Value Share Analysis, by Architecture Industry 13.8. Europe Deep Learning Market Forecast, by Architecture Industry 13.8.1. RNN 13.8.2. CNN 13.8.3. DBN 13.8.4. DSN 13.8.5. GRU 13.9. Europe Deep Learning Market Value Share Analysis, by End Use Industry 13.10. Europe Deep Learning Market Forecast, by End Use Industry 13.10.1. Healthcare 13.10.2. Automotive 13.10.3. Media & Entertainment 13.10.4. BFSI 13.10.5. Other 13.11. Europe Deep Learning Market Value Share Analysis, by Country 13.12. Europe Deep Learning Market Forecast, by Country 13.12.1. Germany 13.12.2. U.K. 13.12.3. France 13.12.4. Italy 13.12.5. Spain 13.12.6. Rest of Europe 13.13. Europe Deep Learning Market Analysis, by Country 13.14. Germany Deep Learning Market Forecast, by Component 13.14.1. Hardware 13.14.2. Software 13.15. Germany Deep Learning Market Forecast, by Application 13.15.1. Speech Recognition 13.15.2. Image Recognition 13.15.3. Data Mining 13.15.4. Drug Discovery 13.15.5. Driver Assistance 13.15.6. Others 13.16. Germany Deep Learning Market Forecast, by Architecture Industry 13.16.1. RNN 13.16.2. CNN 13.16.3. DBN 13.16.4. DSN 13.16.5. GRU 13.17. Germany Deep Learning Market Forecast, by End Use Industry 13.17.1. Healthcare 13.17.2. Automotive 13.17.3. Media & Entertainment 13.17.4. BFSI 13.17.5. Other 13.18. U.K. Deep Learning Market Forecast, by Component 13.18.1. Hardware 13.18.2. Software 13.19. U.K. Deep Learning Market Forecast, by Application 13.19.1. Speech Recognition 13.19.2. Image Recognition 13.19.3. Data Mining 13.19.4. Drug Discovery 13.19.5. Driver Assistance 13.19.6. Others 13.20. U.K. Deep Learning Market Forecast, by Architecture Industry 13.20.1. RNN 13.20.2. CNN 13.20.3. DBN 13.20.4. DSN 13.20.5. GRU 13.21. U.K. Deep Learning Market Forecast, by End Use Industry 13.21.1. Healthcare 13.21.2. Automotive 13.21.3. Media & Entertainment 13.21.4. BFSI 13.21.5. Other 13.22. France Deep Learning Market Forecast, by Component 13.22.1. Hardware 13.22.2. Software 13.23. France Deep Learning Market Forecast, by Application 13.23.1. Speech Recognition 13.23.2. Image Recognition 13.23.3. Data Mining 13.23.4. Drug Discovery 13.23.5. Driver Assistance 13.23.6. Others 13.24. France Deep Learning Market Forecast, by Architecture Industry 13.24.1. RNN 13.24.2. CNN 13.24.3. DBN 13.24.4. DSN 13.24.5. GRU 13.25. France Deep Learning Market Forecast, by End Use Industry 13.25.1. Healthcare 13.25.2. Automotive 13.25.3. Media & Entertainment 13.25.4. BFSI 13.25.5. Other 13.26. Italy Deep Learning Market Forecast, by Component 13.26.1. Hardware 13.26.2. Software 13.27. Italy Deep Learning Market Forecast, by Application 13.27.1. Speech Recognition 13.27.2. Image Recognition 13.27.3. Data Mining 13.27.4. Drug Discovery 13.27.5. Driver Assistance 13.27.6. Others 13.28. Italy Deep Learning Market Forecast, by Architecture Industry 13.28.1. RNN 13.28.2. CNN 13.28.3. DBN 13.28.4. DSN 13.28.5. GRU 13.29. Italy Deep Learning Market Forecast, by End Use Industry 13.29.1. Healthcare 13.29.2. Automotive 13.29.3. Media & Entertainment 13.29.4. BFSI 13.29.5. Other 13.30. Spain Deep Learning Market Forecast, by Component 13.30.1. Hardware 13.30.2. Software 13.31. Spain Deep Learning Market Forecast, by Application 13.31.1. Speech Recognition 13.31.2. Image Recognition 13.31.3. Data Mining 13.31.4. Drug Discovery 13.31.5. Driver Assistance 13.31.6. Others 13.32. Spain Deep Learning Market Forecast, by Architecture Industry 13.32.1. RNN 13.32.2. CNN 13.32.3. DBN 13.32.4. DSN 13.32.5. GRU 13.33. Spain Deep Learning Market Forecast, by End Use Industry 13.33.1. Healthcare 13.33.2. Automotive 13.33.3. Media & Entertainment 13.33.4. BFSI 13.33.5. Other 13.34. Rest of Europe Deep Learning Market Forecast, by Component 13.34.1. Hardware 13.34.2. Software 13.35. Rest of Europe Deep Learning Market Forecast, by Application 13.35.1. Speech Recognition 13.35.2. Image Recognition 13.35.3. Data Mining 13.35.4. Drug Discovery 13.35.5. Driver Assistance 13.35.6. Others 13.36. Rest of Europe Deep Learning Market Forecast, by Architecture Industry 13.36.1. RNN 13.36.2. CNN 13.36.3. DBN 13.36.4. DSN 13.36.5. GRU 13.37. Rest Of Europe Deep Learning Market Forecast, by End Use Industry 13.37.1. Healthcare 13.37.2. Automotive 13.37.3. Media & Entertainment 13.37.4. BFSI 13.37.5. Other 13.38. Europe Deep Learning Market Attractiveness Analysis 13.38.1. By Component 13.38.2. By Application 13.38.3. By Architecture Industry 13.38.4. By End Use Industry 13.39. PEST Analysis 13.40. Key Trends 13.41. Key Development 14. Asia Pacific Deep Learning Market Analysis 14.1. Key Findings 14.2. Asia Pacific Deep Learning Market Overview 14.3. Asia Pacific Deep Learning Market Value Share Analysis, by Component 14.4. Asia Pacific Deep Learning Market Forecast, by Component 14.4.1. Hardware 14.4.2. Software 14.5. Asia Pacific Deep Learning Market Value Share Analysis, by Application 14.6. Asia Pacific Deep Learning Market Forecast, by Application 14.6.1. Speech Recognition 14.6.2. Image Recognition 14.6.3. Data Mining 14.6.4. Drug Discovery 14.6.5. Driver Assistance 14.6.6. Others 14.7. Asia Pacific Deep Learning Market Value Share Analysis, by Architecture Industry 14.8. Asia Pacific Deep Learning Market Forecast, by Architecture Industry 14.8.1. RNN 14.8.2. CNN 14.8.3. DBN 14.8.4. DSN 14.8.5. GRU 14.9. Asia Pacific Deep Learning Market Value Share Analysis, by End Use Industry 14.10. Asia Pacific Deep Learning Market Forecast, by End Use Industry 14.10.1. Healthcare 14.10.2. Automotive 14.10.3. Media & Entertainment 14.10.4. BFSI 14.10.5. Other 14.11. Asia Pacific Deep Learning Market Value Share Analysis, by Country 14.12. Asia Pacific Deep Learning Market Forecast, by Country 14.12.1. China 14.12.2. India 14.12.3. Japan 14.12.4. ASEAN 14.12.5. Rest of Asia Pacific 14.13. Asia Pacific Deep Learning Market Analysis, by Country 14.14. China Deep Learning Market Forecast, by Component 14.14.1. Hardware 14.14.2. Software 14.15. China Deep Learning Market Forecast, by Application 14.15.1. Speech Recognition 14.15.2. Image Recognition 14.15.3. Data Mining 14.15.4. Drug Discovery 14.15.5. Driver Assistance 14.15.6. Others 14.16. China Deep Learning Market Forecast, by Architecture Industry 14.16.1. RNN 14.16.2. CNN 14.16.3. DBN 14.16.4. DSN 14.16.5. GRU 14.17. China Deep Learning Market Forecast, by End Use Industry 14.17.1. Healthcare 14.17.2. Automotive 14.17.3. Media & Entertainment 14.17.4. BFSI 14.17.5. Other 14.18. India Deep Learning Market Forecast, by Component 14.18.1. Hardware 14.18.2. Software 14.19. India Deep Learning Market Forecast, by Application 14.19.1. Speech Recognition 14.19.2. Image Recognition 14.19.3. Data Mining 14.19.4. Drug Discovery 14.19.5. Driver Assistance 14.19.6. Others 14.20. India Deep Learning Market Forecast, by Architecture Industry 14.20.1. RNN 14.20.2. CNN 14.20.3. DBN 14.20.4. DSN 14.20.5. GRU 14.21. India Deep Learning Market Forecast, by End Use Industry 14.21.1. Healthcare 14.21.2. Automotive 14.21.3. Media & Entertainment 14.21.4. BFSI 14.21.5. Other 14.22. Japan Deep Learning Market Forecast, by Component 14.22.1. Hardware 14.22.2. Software 14.23. Japan Deep Learning Market Forecast, by Application 14.23.1. Speech Recognition 14.23.2. Image Recognition 14.23.3. Data Mining 14.23.4. Drug Discovery 14.23.5. Driver Assistance 14.23.6. Others 14.24. Japan Deep Learning Market Forecast, by Architecture Industry 14.24.1. RNN 14.24.2. CNN 14.24.3. DBN 14.24.4. DSN 14.24.5. GRU 14.25. Japan Deep Learning Market Forecast, by End Use Industry 14.25.1. Healthcare 14.25.2. Automotive 14.25.3. Media & Entertainment 14.25.4. BFSI 14.25.5. Other 14.26. ASEAN Deep Learning Market Forecast, by Component 14.26.1. Hardware 14.26.2. Software 14.27. ASEAN Deep Learning Market Forecast, by Application 14.27.1. Speech Recognition 14.27.2. Image Recognition 14.27.3. Data Mining 14.27.4. Drug Discovery 14.27.5. Driver Assistance 14.27.6. Others 14.28. ASEAN Deep Learning Market Forecast, by Architecture Industry 14.28.1. RNN 14.28.2. CNN 14.28.3. DBN 14.28.4. DSN 14.28.5. GRU 14.29. ASEAN Deep Learning Market Forecast, by End Use Industry 14.29.1. Healthcare 14.29.2. Automotive 14.29.3. Media & Entertainment 14.29.4. BFSI 14.29.5. Other 14.30. Rest of Asia Pacific Deep Learning Market Forecast, by Component 14.30.1. Hardware 14.30.2. Software 14.31. Rest of Asia Pacific Deep Learning Market Forecast, by Application 14.31.1. Speech Recognition 14.31.2. Image Recognition 14.31.3. Data Mining 14.31.4. Drug Discovery 14.31.5. Driver Assistance 14.31.6. Others 14.32. Rest of Asia Pacific Deep Learning Market Forecast, by Architecture Industry 14.32.1. RNN 14.32.2. CNN 14.32.3. DBN 14.32.4. DSN 14.32.5. GRU 14.33. Rest of Asia Pacific Deep Learning Market Forecast, by End Use Industry 14.33.1. Healthcare 14.33.2. Automotive 14.33.3. Media & Entertainment 14.33.4. BFSI 14.33.5. Other 14.34. Asia Pacific Deep Learning Market Attractiveness Analysis 14.34.1. By Component 14.34.2. By Application 14.34.3. By Architecture Industry 14.34.4. By End Use Industry 14.35. PEST Analysis 14.36. Key Trends 14.37. Key Development 15. Middle East & Africa Deep Learning Market Analysis 15.1. Key Findings 15.2. Middle East & Africa Deep Learning Market Overview 15.3. Middle East & Africa Deep Learning Market Value Share Analysis, by Component 15.4. Middle East & Africa Deep Learning Market Forecast, by Component 15.4.1. Hardware 15.4.2. Software 15.5. Middle East & Africa Deep Learning Market Value Share Analysis, by Application 15.6. Middle East & Africa Deep Learning Market Forecast, by Application 15.6.1. Speech Recognition 15.6.2. Image Recognition 15.6.3. Data Mining 15.6.4. Drug Discovery 15.6.5. Driver Assistance 15.6.6. Others 15.7. Middle East & Africa Deep Learning Market Value Share Analysis, by Architecture Industry 15.8. Middle East & Africa Deep Learning Market Forecast, by Architecture Industry 15.8.1. RNN 15.8.2. CNN 15.8.3. DBN 15.8.4. DSN 15.8.5. GRU 15.9. Middle East & Africa Deep Learning Market Value Share Analysis, by End Use Industry 15.10. Middle East & Africa Deep Learning Market Forecast, by End Use Industry 15.10.1. Healthcare 15.10.2. Automotive 15.10.3. Media & Entertainment 15.10.4. BFSI 15.10.5. Other 15.11. Middle East & Africa Deep Learning Market Value Share Analysis, by Country 15.12. Middle East & Africa Deep Learning Market Forecast, by Country 15.12.1. GCC 15.12.2. South Africa 15.12.3. Rest of Middle East & Africa 15.13. Middle East & Africa Deep Learning Market Analysis, by Country 15.14. GCC Deep Learning Market Forecast, by Component 15.14.1. Hardware 15.14.2. Software 15.15. GCC Deep Learning Market Forecast, by Application 15.15.1. Speech Recognition 15.15.2. Image Recognition 15.15.3. Data Mining 15.15.4. Drug Discovery 15.15.5. Driver Assistance 15.15.6. Others 15.16. GCC Deep Learning Market Forecast, by Architecture Industry 15.16.1. RNN 15.16.2. CNN 15.16.3. DBN 15.16.4. DSN 15.16.5. GRU 15.17. GCC Deep Learning Market Forecast, by End Use Industry 15.17.1. Healthcare 15.17.2. Automotive 15.17.3. Media & Entertainment 15.17.4. BFSI 15.17.5. Other 15.18. South Africa Deep Learning Market Forecast, by Component 15.18.1. Hardware 15.18.2. Software 15.19. South Africa Deep Learning Market Forecast, by Application 15.19.1. Speech Recognition 15.19.2. Image Recognition 15.19.3. Data Mining 15.19.4. Drug Discovery 15.19.5. Driver Assistance 15.19.6. Others 15.20. South Africa Deep Learning Market Forecast, by Architecture Industry 15.20.1. RNN 15.20.2. CNN 15.20.3. DBN 15.20.4. DSN 15.20.5. GRU 15.21. South Africa Deep Learning Market Forecast, by End Use Industry 15.21.1. Healthcare 15.21.2. Automotive 15.21.3. Media & Entertainment 15.21.4. BFSI 15.21.5. Other 15.22. Rest of Middle East & Africa Deep Learning Market Forecast, by Component 15.22.1. Hardware 15.22.2. Software 15.23. Rest of Middle East & Africa Deep Learning Market Forecast, by Application 15.23.1. Speech Recognition 15.23.2. Image Recognition 15.23.3. Data Mining 15.23.4. Drug Discovery 15.23.5. Driver Assistance 15.23.6. Others 15.24. Rest of Middle East & Africa Deep Learning Market Forecast, by Architecture Industry 15.24.1. RNN 15.24.2. CNN 15.24.3. DBN 15.24.4. DSN 15.24.5. GRU 15.25. Rest of Middle East & Africa Deep Learning Market Forecast, by End Use Industry 15.25.1. Healthcare 15.25.2. Automotive 15.25.3. Media & Entertainment 15.25.4. BFSI 15.25.5. Other 15.26. Middle East & Africa Deep Learning Market Attractiveness Analysis 15.26.1. By Component 15.26.2. By Application 15.26.3. By Architecture Industry 15.26.4. By End Use Industry 15.27. PEST Analysis 15.28. Key Trends 15.29. Key Development 16. South America Deep Learning Market Analysis 16.1. Key Findings 16.2. South America Deep Learning Market Overview 16.3. South America Deep Learning Market Value Share Analysis, by Component 16.4. South America Deep Learning Market Forecast, by Component 16.4.1. Hardware 16.4.2. Software 16.5. South America Deep Learning Market Value Share Analysis, by Application 16.6. South America Deep Learning Market Forecast, by Application 16.6.1. Speech Recognition 16.6.2. Image Recognition 16.6.3. Data Mining 16.6.4. Drug Discovery 16.6.5. Driver Assistance 16.6.6. Others 16.7. South America Deep Learning Market Value Share Analysis, by Architecture Industry 16.8. South America Deep Learning Market Forecast, by Architecture Industry 16.8.1. RNN 16.8.2. CNN 16.8.3. DBN 16.8.4. DSN 16.8.5. GRU 16.9. South America Deep Learning Market Value Share Analysis, by End Use Industry 16.10. South America Deep Learning Market Forecast, by End Use Industry 16.10.1. Healthcare 16.10.2. Automotive 16.10.3. Media & Entertainment 16.10.4. BFSI 16.10.5. Other 16.11. South America Deep Learning Market Value Share Analysis, by Country 16.12. South America Deep Learning Market Forecast, by Country 16.12.1. Brazil 16.12.2. Mexico 16.12.3. Rest of South America 16.13. South America Deep Learning Market Analysis, by Country 16.14. Brazil Deep Learning Market Forecast, by Component 16.14.1. Hardware 16.14.2. Software 16.15. Brazil Deep Learning Market Forecast, by Application 16.15.1. Speech Recognition 16.15.2. Image Recognition 16.15.3. Data Mining 16.15.4. Drug Discovery 16.15.5. Driver Assistance 16.15.6. Others 16.16. Brazil Deep Learning Market Forecast, by Architecture Industry 16.16.1. RNN 16.16.2. CNN 16.16.3. DBN 16.16.4. DSN 16.16.5. GRU 16.17. Brazil Deep Learning Market Forecast, by End Use Industry 16.17.1. Healthcare 16.17.2. Automotive 16.17.3. Media & Entertainment 16.17.4. BFSI 16.17.5. Other 16.18. Mexico Deep Learning Market Forecast, by Component 16.18.1. Hardware 16.18.2. Software 16.19. Mexico Deep Learning Market Forecast, by Application 16.19.1. Speech Recognition 16.19.2. Image Recognition 16.19.3. Data Mining 16.19.4. Drug Discovery 16.19.5. Driver Assistance 16.19.6. Others 16.20. Mexico Deep Learning Market Forecast, by Architecture Industry 16.20.1. RNN 16.20.2. CNN 16.20.3. DBN 16.20.4. DSN 16.20.5. GRU 16.21. Mexico Deep Learning Market Forecast, by End Use Industry 16.21.1. Healthcare 16.21.2. Automotive 16.21.3. Media & Entertainment 16.21.4. BFSI 16.21.5. Other 16.22. Rest of South America Deep Learning Market Forecast, by Component 16.22.1. Hardware 16.22.2. Software 16.23. Rest of South America Deep Learning Market Forecast, by Application 16.23.1. Speech Recognition 16.23.2. Image Recognition 16.23.3. Data Mining 16.23.4. Drug Discovery 16.23.5. Driver Assistance 16.23.6. Others 16.24. Rest of South America Deep Learning Market Forecast, by Architecture Industry 16.24.1. RNN 16.24.2. CNN 16.24.3. DBN 16.24.4. DSN 16.24.5. GRU 16.25. Rest of South America Deep Learning Market Forecast, by End Use Industry 16.25.1. Healthcare 16.25.2. Automotive 16.25.3. Media & Entertainment 16.25.4. BFSI 16.25.5. Other 16.26. South America Deep Learning Market Attractiveness Analysis 16.26.1. By Component 16.26.2. By Application 16.26.3. By Architecture Industry 16.26.4. By End Use Industry 16.27. PEST Analysis 16.28. Key Trends 16.29. Key Development 17. Company Profiles 17.1. Market Share Analysis, by Company 17.2. Competition Matrix 17.2.1. Competitive Benchmarking of key players by price, presence, market share, Applications and R&D investment 17.2.2. New Product Launches and Product Enhancements 17.2.3. Market Consolidation 17.2.3.1. M&A by Regions, Investment and Applications 17.2.3.2. M&A Key Players, Forward Integration and Backward Integration 17.3. Company Profiles: Key Players 17.3.1. Advanced Micro Devices, Inc 17.3.1.1. Company Overview 17.3.1.2. Financial Overview 17.3.1.3. Product Portfolio 17.3.1.4. Business Strategy 17.3.1.5. Recent Developments 17.3.1.6. Company Footprint 17.3.2. Arm Ltd. 17.3.3. Baidu Inc. 17.3.4. Clairifai Inc. 17.3.5. Enlitic 17.3.6. General Vision Inc. 17.3.7. Google Inc. 17.3.8. Hewlett Packard 17.3.9. IBM Corporation 17.3.10. Intel Corporation 17.3.11. Microsoft Corporation 17.3.12. Nvidia Corporation 17.3.13. Qualcomm Technologies Inc. 17.3.14. Sensory Inc. 17.3.15. Skymind 17.3.16. Alphabet Inc. 17.3.17. Micron Technology Inc. 17.3.18. Amazon Web Services 17.3.19. Graphcore 17.3.20. Xilinx 17.3.21. AWS 17.3.22. Sensory Inc. 17.3.23. Mellanox Technologies 18. Primary Key Insights

About This Report

Report ID25018
Category Information Technology & Telecommunication
Published DateJAN 2020
Updated DateSep 2021
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