Deep learning technology is also known as hierarchical learning or deep structured learning is a type of machine learning and involves learning data representation. The global deep learning market is expected to have a CAGR of 65.4% during the forecasted period of 2016-2023. The key factor responsible for the surging demand of deep learning in healthcare applications is the increasing usage of big data in healthcare industry. The medical research institutions and other academic institutions are using big data analytics for healthcare and medical research. This raw (unprocessed) big data is processed by big data analytics tools and analysed by data scientists, graduates, statisticians etc. The major application of big data in healthcare is Electronic Health Records (EHR). Every patient has its own digital record which comprises of medical history, allergies, demographics, laboratory test results etc. According to the Health Information Technology for Economic and Clinical Health (HITECH), by the end of 2016, 94% of hospitals in the U.S. have adopted the concept of the Electronic Health Records. According to National Center for Biotechnology Information (NCBI), the amount of big data in healthcare is expected to reach up to approximately 25,000 peta bytes by the end of 2020. Furthermore, big data applications in healthcare present new opportunities to discover new knowledge and create methods in order to enhance the quality of health care services. Therefore, the increasing usage of big data in healthcare industry is driving the growth of deep learning market. The enhancing demand for improved system and human interaction will boost the opportunities of growth in the deep learning market. The systems include a process in which they provide a deep domain view and deliver this information or data to the end users industry which includes IT & telecom, Medical, automotive and agriculture sector. For instance: in financial sector, the deep learning systems benefits the bank employees to enhance their work ability and focus on the customer behaviour & interaction. For instance: Deep learning is used in BFSI sector for risk management, fraud analytics, performance evaluation etc. The deep learning system also help in adapting the self-service and customized options for the consumers and support the employees in providing the materialistic recommendations to fulfil the customers need and controlling risks.
Source-OBRC Analysis
The report is widely categorized on the basis of market segments which include offerings, applications and end-user industry.
Applications of deep learning include:
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Image Recognition
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Signal Recognition
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Data Mining
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Others
Offerings of deep learning include:
End-users of deep learning include:
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Aerospace & Defense
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IT & Telecom
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Medical
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Automotive
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Industrial
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Media & Advertising
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Finance
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Retail
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Oil, Gas and Energy
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Others
The global report on this market is geographically segmented into:
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North America (U.S. & Canada)
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Asia Pacific (China, India, Japan, RoAPAC)
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Europe (UK, France, Germany, RoE)
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Rest of World
Geographically, North America is the largest market region for global deep learning market in terms of market revenue share. The key factors driving the market growth in North America is the exponential rise in the volumes of generated unstructured data generation on the by various industries verticals which includes financial services & banking, insurance, healthcare, retail and public sector. Moreover, the U.S. government is investing in the defence sector and the deep learning technology is being extensively used in the aerospace and defense industry which is expected to positively influence the market growth in North America. For instance: in 2015, as per National Priorities Project, US military and defence spending was estimated to be $598.5 billion in 2015. Moreover, the presence of leading companies in U.S. such as IBM Corporation, Google, Apple Inc., Hewlett Packard etc. would drive the market growth. However, Asia Pacific is expected to emerge as the fastest growing market region during the forecast period 2016-2023 owing to the increasing government expenditure on the research and development of artificial intelligence and cognitive computing technologies.
The global report on this market covers segmentation analysis of offerings, applications and end-user industry. Report further covers segments of offerings which include hardware and software. Software is the dominating offering segment owing to the surging demand for deep learning solutions across various applications such as data analytics, autonomous car, cyber security, fraud detection etc. Hardware is the fastest offering segment surging demand for hardware platforms with a high computing power to run and execute deep learning algorithms. Report further covers segments of applications which include image recognition, signal recognition, data mining and others. Image recognition is the leading application segment as this technology is being extensively used pattern recognition, code recognition, optical character recognition, object recognition, digital image processing and facial recognition. While, data mining is the fastest growing application segment owing to its extensive adoption in data analytics, fraud detection, cyber security and database systems. Whereas, the end users of deep learning include Aerospace & Defense, IT & Telecom, medical, automotive, industrial, media & advertising, finance, retail, oil, gas and energy. Medical is the fastest growing end user segment due to the extensive deployment of deep learning technology in drug discovery, processing medical images for diagnosis and treatment of chronic diseases such as cancer etc., and also to provide virtual patient assistance.
The major market players are:
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IBM CORPORATION
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MICROSOFT CORPORATION
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GOOGLE INC.
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INTEL CORPORATION
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HEWLETT PACKARD
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OTHERS
These companies using various strategies such as merger & acquisition, collaboration, partnership and product launch. Whereas, product and service launch are the key strategy adopted by the companies in the deep learning market.
For example: In August 2017, IBM Corporation launched software named Distributed Deep Learning (DDL) software library to speed up the deep learning process. Moreover, this deep learning software is a part of IBM’s power artificial intelligence.
In April 2016, Hewlett Packard had announced the launch of its new workload-optimized compute platforms and solutions in order to help the customers accelerate innovation and time-to-value with deep learning systems to be used in high-performance computing and financial services industry applications.
The report covers detailed analysis of companies which comprises overview, SCOT analysis, product portfolio, strategic initiative, strategic analysis, competitive landscape and market share analysis.
Key reason to buy the report:
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The report includes market estimation, forecast and analysis for the year 2016-2023.
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Report includes detailed analysis of different segments such as offering, applications and end users of deep learning.
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Identify and understand the strength, opportunities, challenges and threats.
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Covers details analysis of Porters 5 force model and other strategic models and also covers revenues, market share analysis, and competitive landscape analysis of major players.
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Detailed analysis of various the regulatory policies which are affecting the market.
How we are different from others:
At Occam’s we provide an extensive portfolio which is comprehensive market analysis along with the market size, market share, and market segmentations. Our global report on this market offers detailed analysis of strategic models such as investment vs. adoption model, see saw analysis and others strategic models. Also, the report contains the detailed analysis of application, adoption scenario and decision support for each segment. The report discusses competitive landscape, with giving extensive SCOT analysis of key companies.
Key findings:
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North America is the largest market region in terms of market revenue share.
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Deep learning software is the dominating offering segment.
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Whereas, product and service launch are the key strategy adopted by the companies.
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