Deep Learning Market, By Component (Hardware (network, memory, processor), Software (Solution, Platform), Service (Installation, Integration, Maintenance) By Application (Single recognition, image recognition, data mining and others) By End use (Automotive, electrical and electronics, Healthcare, Agriculture, Aerospace and Defense, Others – Global Insights, Trends and Forecast, 2017 - 2025

Created On: Dec-2018
Report ID: IR193
Format: PDF

Market Overview

Deep learning also known as hierarchical learning or deep structured learning is a subset of machine learning. It is a type of artificial intelligence that works exactly like the human brain by processing data and creating patterns for decision marking. The growing adoption of cloud-based technology across various end use industries is a major factor driving the growth of the deep learning market during the forecast period. Deep learning technologies are extensively used over various verticals including phones, cars, tablets, televisions, medical devices and others.

Market Dynamics

The growing implementations of Deep Learning in cloud-based technology coupled with increasing adoption in the big data analytics is projected to substantially boost the growth of the market during the forecast period. Moreover, with the growing expenditure in the healthcare industry, travel and tourism industry is expected to be a major opportunity in the growth of the global deep learning market during the review period. However, lack of technical expertise along with the absence of standard protocol are expected to be the major restraints in the growth of the global deep learning market.

Segmentation Analysis

On the basis of component, the global Deep Learning market has been segmented into hardware, software and service. Among the aforementioned segments the software segment is projected to generate the highest gains during the forecast period. The growing use of deep learning software in cars and electronics is projected to positively contribute to the growth of the segment.

On the basis of Application, the global Deep Learning market has been segmented into, Single recognition, image recognition, data mining and others. Among these, data mining segment and is projected to show the highest CAGR during the forecast period. The growing use of data mining, machine translation, sentiment analysis, fingerprint identification, bioinformatics and cybersecurity are expected to substantially augment the growth of the overall deep learning market during the given period.

Regional Outlook

On the basis of region, the global Deep Learning Market can be segregated into North America, Europe, Asia pacific, Latin America, Middle East and Africa. According to insights and reports analysis, the North American Deep Learning Market is projected to generate the highest revenue during the forecast period. The presence of high technological infrastructure coupled with the increasing adoption of deep learning in various industries such as defense and security are projected drive the market in this region with U.S being the largest contributor. Moreover, the presence of major deep learning companies in the region is also expected to add to the deep learning market growth.

Asia Pacific is projected to show the highest CAGR during the forecast period. The developing technological infrastructure in the region mainly in China, Japan and India is projected to drive the market in these regions. Furthermore, the shift of major automobile manufacturers in the region is projected to increase the adoption of deep learning technologies in automobiles which is another factor expected to drive the deep learning market growth during the forecast period.

Competitive Landscape

Some of the proficient players operating in the global Deep Learning Market include, NVIDIA (US), Xilinx (US), Intel (US), Micron Technology (US), Samsung Electronics (South Korea), IBM (US), Qualcomm (US), Google (US), Microsoft (US), and AWS (US) among others.

Our Research Approach Includes
  •  Market Outlining
  •  Framing discussion guide
  •  Data Validation
  • Data Analysis
  •  Re-Validation and Finalization of Data
  •  Report Insights and Publishing
Our Research Approach Includes
  •  Market Outlining
  •  Framing discussion guide
  •  Data Validation
  • Data Analysis
  •  Re-Validation and Finalization of Data
  •  Report Insights and Publishing
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