Deep learning can be considered as a subset of machine learning and consists of algorithms that allow a software to self-train to execute tasks such as image and speech recognition by exposing multi-layered neural networks to bulk data. It can have a profound impact on various industries such as finance, automotive, aerospace, telecommunication and information technology, oil and gas, industrial, defense, media and advertising, medical and others. The increasing research and development activities in this domain is expanding the end use areas for the technology. The factors that contribute to the high market share are parallelization, high computing power, swift improvements in information storage capacity in automotive and healthcare industries. A few major applications for deep learning systems are in autonomous cars, data analytics, cyber security and fraud detection. It has become imperative for both small and big organizations to analyse and extract meaningful information from visual content. Advanced technologies such as graphic processing units are highly accepted in scientific disciplines such as deep learning and data sciences. Valuable insights are extracted from bulk data by using deep learning neural networks to improve customer experience and generate innovative products. The development in artificial intelligence capabilities in natural language processing, computer vision areas and image and speech recognition are driving the growth for deep learning.
The use cases for deep learning is diverse ranging from detecting gene abnormalities predicting weather patterns to identifying fraudulent insurance claims, stock market analysis, robotics, drones, finance, agriculture. Deep learning systems have wide applications in the banking and financial sector. It helps bank employees expand their capabilities so that they can focus more on customer interactions rather than regular banking transactions. The deep learning software can offer solutions based on a client’s background and history and thus can provide evidence and context-based reasoning for every problem. Industries worldwide are generating enormous data which require high processing power and this data is being generated at an unprecedented rate and volume. This has created an enormous opportunity for deep learning powered applications. A plethora of start-ups are coming up with vertical specific solutions and global corporations are supporting these start-ups to innovate faster.
According to Infoholic Research, the Global Deep Learning market is expected to grow at a CAGR of 49.93% during the forecast period 2017–2023. The market is driven by factors such as faster processor performance, large training data size, and sophisticated neural nets. The future potential of the market is promising owing to opportunities such as development in big data technologies, expanding end-user base and extensive R&D. The market growth is curbed by restraining factors such as implementation challenges, rigid business models, dearth of skilled data scientists, affordability of organizations and data security concerns and inaccessibility.
Segmentation by Solutions
The market has been segmented and analyzed by the following components: Software and Hardware.
Segmentation by End-Users
The market has been segmented and analyzed by the following end-users: Medical, Automotive, Retail, Finance, IT & Telecommunications, Industrial, Aerospace and Defence, Media and Advertising, Oil, Gas and Energy and Others.
Segmentation by Regions
The market has been segmented and analyzed by the following regions: North America, EMEA, Latin America, APAC and Latin America.
Segmentation by Applications
The market has been segmented and analyzed by the following applications: Image Recognition, Voice Recognition, Video Surveillance and Diagnostics, Data mining and Others.
The study covers and analyses the “Global Deep Learning Market”. Bringing out the complete key insights of the industry, the report aims to provide an opportunity for players to understand the latest trends, current market scenario, government initiatives, and technologies related to the market. In addition, it helps the venture capitalists in understanding the companies better and take informed decisions.
- The report covers drivers, restraints, and opportunities (DRO) affecting the market growth during the forecast period (2017–2023).
- It also contains an analysis of vendor profiles, which include financial health, business units, key business priorities, SWOT, strategies, and views.
- The report covers competitive landscape, which includes M&A, joint ventures and collaborations, and competitor comparison analysis.
- In the vendor profile section, for the companies that are privately held, financial information and revenue of segments will be limited.