The value chain analysis helps to analyze major raw materials, major equipment, manufacturing processes, customer analysis and major Deep Learning in Computer Vision distributors. The report also provides Porter analysis, PESTEL analysis and market attractiveness, which helps to better understand the market scenario at macro and micro level. It also provides explicit information about fusions, acquisitions, joint ventures and other important market activities in recent years. The report also covers a detailed description, a competitive scenario, a wide range of market leaders and business strategies adopted by competitors with their SWOT analysis. Data on the consumer perspective, comprehensive analysis, statistics, market share, company performance, historical analysis from 2016 to 2017, volume, revenue, YOY growth rate and CAGR forecast to 2025 are included in the report.
The Global Deep Learning in Computer Vision Market accounted for USD 7.8 billion in 2017 and is projected to grow at a CAGR of 55.7% the forecast period of 2018 to 2025. The upcoming market report contains data for historic years 2016, the base year of calculation is 2017 and the forecast period is 2018 to 2025.
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The global deep learning in computer vision market is fragmented and the major players have used various strategies such as new product launches, expansions, agreements, joint ventures, partnerships, acquisitions, and others to increase their footprints in this market in order to sustain in long run. The report includes market shares of deep learning in computer vision market for global, Europe, North America, Asia Pacific and South America.
Global Deep Learning in Computer Vision Market By Solutions (Hardware, Software , Services), By Hardware (Central Processing Unit (CPU), Graphics Processing Unit (GPU), Others), By Application (Image recognition, Voice recognition ,Others), By End-user (Automotive, Healthcare , Others) ,By Geographical Segments (North America, South America, Europe, Asia-Pacific, Middle East and Africa)- Industry Trends and Forecast to 2025
The renowned players in deep learning in computer vision market are Accenture, Applariat, Appveyor, Atlassian, Bitrise, CA Technologies, Chef Software, Circleci, Clarive, Cloudbees, Electric Cloud, Flexagon, Heroku, IBM, Infostretch, Jetbrains, Kainos, Micro Focus, Microsoft, Puppet Enterprise, Red Hat, Shippable, Spirent, VMware, Wipro and Xebialabs among others.
Market Drivers and Restraints:
• Rapid improvements in fast information storage capacity.
• High computing power and parallelization.
• Need for quality check and automation is increasing.
• Lack of technical expertise.
• Lack of user awareness about rapidly changing computer vision technology for deep learning.
For a pervasive understanding of the market, business strategies and latest developments of the vital players accompanied with co-development deals and market size have also been enclosed. Briefly citing, their revenue share, contact information and meticulous SWOT analysis is available. The regions which have been studied in depth are North America, Europe, Asia Pacific, Middle East & Africa and Latin America. This helps gain better idea about the spread of this particular market in respective regions.
The report takes a close and analytical look at the various companies that strive for a higher share of the Deep Learning in Computer Vision Market. Data on the leading and fastest-growing segments along with what drives them has been given.
This report implements a balanced mix of primary and secondary research methodologies for its analysis. The market is segmented on the basis of key criteria.
A bird’s eye view of the Deep Learning in Computer Vision Industry made available in the report helps readers to understand the key drivers, restraints, challenges, and opportunities that are shaping the Deep Learning in Computer Vision market. Furthermore, the report evaluates challenges experienced from buyers and sellers side.
Key Highlights of Report
• Overview of key market forces propelling and restraining market growth
• Offers a clear understanding of the competitive landscape and key product segments
• An analysis of strategies of major competitors
• Detailed analyses of industry trends
• A well-defined technological growth map with an impact-analysis
• Provides profiles of major competitors of the market.
• Details of their operations, product and services.
• Recent developments and key financial metrics.
Deep learning is an intense machine learning tool that indicates extraordinary execution in numerous fields. One of the best accomplishments of deep learning is protest acknowledgment with Convolutional Neural Networks (CNNs). CNNs’ principle control originates from gaining information portrayals specifically from information in a progressive layer based structure. Over the last years deep learning processes have been shown to outperform traditional machine learning techniques and procedures in several fields, prominently in computer vision.
Some of the most significant deep learning tools used in computer vision system are convolutional neural networks, deep boltzmann machines and deep belief networks, and stacked de-noising auto-encoders. In January 2016, Movidius, a U.S. based company collaborated with Google Inc. to enhance deep learning capabilities on mobile devices. In September 2016, Intel Corporation announced the acquisition of Movidius for improvising its computer vision and deep learning solutions. All the collaborations and partnerships made by the organizations to make advancements in computer vision technology.
Market Segmentation: Global Deep Learning in Computer Vision Market
- The global deep learning in computer vision market can be segmented in solutions, hardware, application, end user and geographical segments.
- Based on solutions, the market can be segmented into hardware, software and services.
- Based on hardware, the market can be segmented into central processing unit (CPU), graphics processing unit (GPU), field programmable gate array (FPGA) and application-specific integrated circuit (ASIC).
- Based on application, the market can be segmented into image recognition, voice recognition, video surveillance & diagnostics and data mining.
- Based on end user, the market can be segmented into automotive, aerospace & defense, healthcare, manufacturing and others.
- Based on geography, the market report covers data points for 28 countries across multiple geographies such as North America, South America, Europe, Asia-Pacific and Middle East & Africa. Some of the major countries covered in this report are U.S., Canada, Germany, France, U.K., Netherlands, Switzerland, Turkey, Russia, China, India, South Korea, Japan, Australia, Singapore, Saudi Arabia, South Africa, and Brazil among others.
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