Deep Learning Market Highlights:
The global market for deep learning 2020 can potentially expand at a rate of 30.87% during the forecast timeframe, which is 2017 to 2023.

The potential of deep learning in recent years has been realized, owing to the advances in hardware as well as algorithms that make use of pattern recognition in a continuous learning loop. This way they are able to conduct tasks without the need for explicit programming code. According to MRFR, most the effective deep learning utilizes the cases that revolve around practical advancements in fields like text analysis, image recognition, fraud prevention, content curation and product recommendations, to name a few.

Deep Learning Market Share, in the next few years, is most likely to give way to the development of more disruptive and powerful applications, such as personalized education, preventative healthcare, and driverless cars. The opportunities stemming from deep learning include a broad range of industries as well as geographies and is especially useful in highly domain-centric markets with massive data sets and ontologies. The technology can also gain immense traction in the applications related to machine perception, with the use of vision and language processing abilities.

Some of the most exciting trends making their way into the global market include the rising use of deep learning technology in industries like advertisement and automotive. Some other important trends are the surge in research and development activities in the field in a bid to offer enhanced processing hardware combined with the mounting requirement for high-computing hardware platforms to perform deep learning algorithms. Surging adoption of cloud-based technology along with the rising use of deep learning in big data analytics also works in favor of the market.

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Leading Players:
Some of the leading players in the deep learning industry are Microsoft Corporation (USA), Advanced Micro Devices Inc. (USA), Adapteva, Inc. (USA), Micron Technology (USA), Amazon Inc. (USA), Xilinx Inc. (USA), Google LLC (USA), Intel Corporation (USA), Samsung Electronics Co Ltd (South Korea), Sensory Inc. (USA), NVIDIA Corporation (USA), Qualcomm Technologies Inc. (USA), Mellanox Technologies (USA), IBM Corporation (USA), Facebook (USA), Tenstorrent (Canada), Baidu Inc (China), and others.

Market Segmentation:
Deep learning market share in the near future is anticipated to increase substantially. To analyze the latest market trends, MRFR has segmented it on the basis of component, application and end user.

The component-based market segments are hardware (processors, memory, and network), services (installation, training, and support & maintenance) and software (solution and platform). Out of these, the hardware segment can take the lead in the global market in the forthcoming years.

Application-wise, the primary segments covered in the report are Signal Recognition, Data Mining, Image Recognition and others.

The significant end-users in the market are Manufacturing, Security, Automotive, Retail, BFSI, Media & Entertainment, Agriculture, Healthcare, and others. The massive demand for deep learning in the security industry is the result of the increasingly evolving cybersecurity ecosystem, given the new forms of cyberattacks cropping up, and the growing need among organizations to tackle these threats in order to safeguard their critical assets.

Regional Insight:
Regionally, the deep learning market has been considered for Asia Pacific (APAC), North America, Europe, and the rest of the world (RoW).

Given the considerable investments made in deep learning applications, systems as well as platforms, North America is all set to be the most lucrative market in the near future. It is projected that the advancements in humanoid robots coupled with the rising applications in image recognition, language translation and video-to-video synthesis could culminate into strong growth of the regional market. The United States (US), Mexico and Canada are the leading markets for deep learning in the region.

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