Deep Learning Market by Component (Hardware, Software, Services) by Application (Image Recognition, Signal Recognition, Data Mining, Others) by End-user (Healthcare, Manufacturing, Automotive, Agriculture, Retail, Security, Human Resources, Marketing, Law, Fintech, Others): Global Industry Size, Share, Statistics and Forecast 2015 - 2027

Deep learning is a type of machine learning that employs a three- or more-layered neural network. These neural networks try but fail to mimic the activity of the human brain, allowing it to "learn" from massive amounts of data. While a single-layer neural network can still make approximations, adding additional hidden layers can help optimize and adjust the network for accuracy. Deep learning is being driven by increased computing power and lower hardware prices, increased acceptance of cloud-based technologies, applications in big data analytics, and expanding AI use in customer-centric services. Deep learning had a market value of $ 2.7 billion in 2020, which is expected to reach $ 21.1 billion by 2027. The market is expected to grow at a CAGR of 34.5% over the forecast period.

Interesting Facts of the Deep Learning Market.

• Deep learning is a type of machine learning itself
• Simply speaking, deep learning mirrors the function of the human brain
• Deep learning function in layers
• Deep learning is expected to bring an AI boom
• Deep learning is a subset of machine learning which, in turn, is a subset of AI
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COVID-19 Impact

Companies are using reports to uncover new financial risks and opportunities due to Covid-19, which is driving demand for artificial intelligence and deep learning. IBM Corporation, for example, is examining consumer behavior and estimating the long-term effects of corona in the coming quarter using tools such as the IBM Watson Tone Analyzer and the IBM ILOG CPLEX. Furthermore, the healthcare industry requires a more secure and scalable cloud infrastructure to protect accurate patient data with speed and flexibility. Advanced technologies such as cloud computing, machine learning, big data analytics, and deep learning are in high demand for patient data analysis during the pandemic.

Image recognition accounts for the largest share in 2020 of the deep learning market

Image recognition accounts for the largest share of the deep learning market in terms of applications. Deep learning is expected to be the fastest growing segment of the data mining industry over the forecast period. Code recognition, digital image processing, facial recognition, object recognition, optical character recognition, and pattern recognition are all becoming more prevalent. As new technologies have emerged, deep learning methodologies have created natural language processing and visual data mining. Sentiment analysis, machine translation, fingerprint identification, cybersecurity, and biology are data mining applications.

North America accounts for the dominant position in the global deep learning market.

North America is expected to account for a significant portion of the global deep learning market due to the continued expansion of significant data volume and the rising demand for the integration of deep learning in corporate consumer-centric solutions. In addition, the growing importance of anticipating critical trends and insights related to customer behavior and operations has been a primary motivator for major corporations to shift their focus toward using artificial intelligence (AI) and big data to create value and provide a personalized experience.

Global Deep Learning Market Key Players:

Xilinx, Skymind, Sensory Inc., Samsung Electronics, Qualcomm, Nvidia, Microsoft Corporation, Micron Technology, International Business Machine, Intel, Google, and Amazon Web Services (AWS) are among the major companies. In addition, transcoder, a system that uses unsupervised deep learning to transform code from one programming language to another, was released in June 2020 by Facebook AI Research. After being trained on over 2.8 million open-source projects, TransCoder outperformed current rule-based code translation algorithms.