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The deep learning market size is forecast to increase by USD 10.85 billion at a CAGR of 26.06% between 2023 and 2028. The market is experiencing significant growth due to the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies in various industries. Key trends include the application of deep learning in medical image analysis for disease diagnosis and drug discovery in healthcare, social media analytics for sentiment analysis on platforms like Instagram and Facebook, and autonomous vehicles for safer transportation. The market is driven by the lack of technical expertise leading to an increase in collaboration among companies and the need for neural networks with multi-layered structures to process big data. Deep learning is revolutionizing sectors such as healthcare, where it is being used to analyze therapeutic areas like oncological disorders, infectious diseases, and neurological disorders, and computing devices are being developed specifically for deep learning applications. Voice recognition technology is another area where deep learning is making a significant impact, enabling more intuitive user interfaces. Despite these opportunities, challenges remain, including the need for large amounts of data for training neural networks and the high computational requirements for deep learning algorithms.
Deep learning, a subset of machine learning and artificial intelligence, is revolutionizing various industries with its ability to learn and improve from experience. The market is driven by the increasing availability of computing power and the adoption of cloud-based technology. Big data analytics is another key factor fueling the market's growth, as deep learning algorithms require vast amounts of data for training. Image recognition, speech recognition, and natural language processing are some of the primary applications of deep learning. These technologies are being integrated into smartphone assistants, ATMs, social networks, and data centers. Deep learning algorithms use a multi-layered neural network architecture, consisting of nodes and neurons, to learn and identify patterns in data.
Further, training algorithms, such as backpropagation and stochastic gradient descent, are used to teach these neural networks. Deep learning is also being used in cybersecurity applications, industrial automation, drug discovery, and diagnostics. Robots and data mining are other areas where deep learning is making a significant impact. Overall, the market is expected to grow significantly due to its potential to transform industries and improve efficiency and accuracy.
The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.
The image recognition segment is estimated to witness significant growth during the forecast period. Deep learning, a subset of artificial intelligence, plays a pivotal role in various industries by enabling signal recognition and precision in data analysis. In the realm of cybersecurity, deep learning is utilized to safeguard critical assets from cyberattacks by monitoring and analyzing patterns to detect anomalies. In media and advertising, deep learning powers recommender systems and image recognition for personalized content delivery in search advertising, social media advertising, and lifestyle management. In the healthcare sector, deep learning is revolutionizing inpatient care, hospital management, medical imaging, diagnostics, and virtual assistants. It is also employed in machinery inspection, production planning, reclamation, quality control, and autonomous driving for enhanced efficiency and productivity.
Furthermore, deep learning is utilized in precision medicine to analyze patient data and provide customized treatment plans. Banks and financial institutions leverage deep learning to analyze extensive and valuable image repositories for fraud detection and risk assessment. The BFSI sector's reliance on deep learning for image recognition is significant, as it facilitates personalized communication with customers, competitive edge in the market, and automation of monotonous tasks. Social media platforms, such as Facebook, employ deep learning for identifying and removing fake accounts and maintaining user security.
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The image recognition segment accounted for USD 1048.50 million in 2018 and showed a gradual increase during the forecast period.
North America is estimated to contribute 36% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.
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In North America, the United States, Canada, and Mexico are leading contributors to the expansion of the market. The primary catalyst fueling this growth is the escalating utilization of deep learning in diverse industrial sectors, including voice recognition, image recognition, and advanced smartphone assistants. The recognition of deep learning's benefits has led to its widespread adoption in these applications. Deep learning algorithms significantly decrease labor expenses, making them an attractive option for organizations. Furthermore, deep learning technology aids in minimizing unnecessary costs related to product recalls by efficiently detecting subtle defects, such as labeling errors. This cutting-edge technology is also making a significant impact in various industries, such as critical asset protection, media and advertising (search and social media), signal recognition, lifestyle management, precision medicine, inpatient care, hospital management, medical imaging, diagnostics, virtual assistant, wearables, machinery inspection, production planning, reclamation, quality control, autonomous driving, and humanâmachine interface.
Our researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.
Increasing entry of startups is the key driver of the market. The market has experienced significant growth in recent years, fueled by advancements in computing power, cloud-based technology, and the availability of vast amounts of data for analysis. This technology, which includes machine learning, image recognition, data mining, pattern recognition, optical character recognition, code recognition, facial recognition, object recognition, digital image processing, natural language processing, visual data mining, sentiment analysis, machine translation, fingerprint identification, cybersecurity, and bioinformatics, has found applications in various industries.
Further, startups have been at the forefront of this innovation, with numerous companies emerging to offer deep learning solutions. Notable examples include Atomwise Inc. And IBM-backed Precision Medicine startup Pathway Genomics Corp., which provide deep learning platforms for the healthcare industry. These companies, among others, are driving the growth of the market by offering advanced solutions that leverage the power of deep learning algorithms to deliver accurate and efficient results.
Increasing collaboration among companies is the upcoming trend in the market. The market is experiencing significant growth due to the increasing collaborations and partnerships among companies. In September 2023, Anthropic and Amazon.Com Inc. Announced a strategic alliance to advance safer generative artificial intelligence (AI) technology. This collaboration will enable Anthropic to expand its product portfolio, geographic reach, and distribution networks, while Amazon will gain access to new technologies and resources, improving its market position. Such strategic initiatives are crucial for market participants to remain competitive in the rapidly evolving deep learning industry.
Additionally, cloud-based technology, big data analytics, and machine learning technology are key drivers of the market. Applications of deep learning include smartphone assistants, ATMs, social networks, image recognition, data mining, pattern recognition, optical character recognition, code recognition, facial recognition, object recognition, digital image processing, natural language processing, visual data mining, sentiment analysis, machine translation, fingerprint identification, cybersecurity, and bioinformatics. These applications are transforming various industries, leading to increased demand for deep learning solutions.
Lack of technical expertise is a key challenge affecting the market growth. Deep learning, a subset of machine learning, is revolutionizing various industries by enabling advanced computational capabilities for big data analytics. With the increasing adoption of cloud-based technology, deep learning models can be deployed to process vast amounts of data for applications such as image recognition, object recognition, facial recognition, code recognition, and natural language processing. These applications are integrated into various systems, including smartphone assistants, ATMs, social networks, and cybersecurity. However, implementing deep learning models requires significant computing power and expertise in machine learning technology and programming languages. The scarcity of professionals with the necessary skills and experience is hindering the growth of the market.
Furthermore, industries like bioinformatics and sentiment analysis in machine translation rely on deep learning for pattern recognition and visual data mining. The limited availability of subject matter specialists and the high cost of hiring them pose challenges for organizations with budget constraints. Consequently, there is a pressing need for organizations to invest in upskilling their workforce or collaborating with external experts to leverage the full potential of deep learning technology.
The market forecasting report includes the adoption lifecycle of the market, covering from the innovator's stage to the laggard's stage. It focuses on adoption rates in different regions based on penetration. Furthermore, the report also includes key purchase criteria and drivers of price sensitivity to help companies evaluate and develop their market growth analysis strategies.
Customer Landscape
Companies are implementing various strategies, such as strategic alliances, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the market.
Amazon.com Inc.: The company offers deep learning solutions, such as applying deep learning in wearable devices and sensors to provide real-time athlete monitoring and performance analysis.
The market research and growth report includes detailed analyses of the competitive landscape of the market and information about key companies, including:
Qualitative and quantitative analysis of companies has been conducted to help clients understand the wider business environment as well as the strengths and weaknesses of key market players. Data is qualitatively analyzed to categorize companies as pure play, category-focused, industry-focused, and diversified; it is quantitatively analyzed to categorize companies as dominant, leading, strong, tentative, and weak.
Deep learning, a subset of machine learning and artificial intelligence, is revolutionizing various industries with its ability to learn and improve from experience. Deep learning algorithms, which are based on neural network architecture and trained using large datasets, are driving advancements in several sectors. Computing power and cloud-based technology have made deep learning more accessible, enabling applications in big data analytics, image recognition, and natural language processing. Deep learning is transforming industries such as healthcare, where it is used for medical image analysis, disease diagnosis, and personalized treatment selection. It is also being adopted in finance for fraud detection, risk assessment, and predictive analytics.
Further, deep learning is being integrated into various technologies, including smartphone assistants, ATMs, social networks, and autonomous vehicles. It is also being used in cybersecurity to detect and prevent cyberattacks on critical assets. In agriculture, deep learning is being used for precision farming, livestock monitoring, and drone analytics. Deep learning is also being used in various industries for monitoring, production planning, quality control, and customer relationship management. It is being used in manufacturing for machinery inspection, production planning, and reclamation. In the healthcare industry, deep learning is being used for inpatient care, hospital management, and medical imaging. Deep learning is also being used in various applications such as facial recognition, object recognition, and sentiment analysis. It is also being used in financial services for stock photography, video websites, and payment services management.
Also, deep learning is also being used in the automotive industry for autonomous driving and humanâmachine interface. Deep learning is also being used in various fields such as bioinformatics, cybersecurity, and precision medicine. It is being used in bioinformatics for drug discovery and molecular data analysis. In cybersecurity, deep learning is being used for encryption, data loss prevention, and intrusion detection. In precision medicine, deep learning is being used for disease indications, prognosis, and targeted treatment selection. Deep learning is being used in various industries to improve efficiency, accuracy, and productivity. It is being used to analyze big data, identify patterns, and make predictions. Deep learning is also being used to develop intelligent systems, such as robots, chatbots, and service bots, that can learn and adapt to new situations. Deep learning is also being used in various applications such as visual search, price optimization, and content curation. It is being used in the retail industry for demand planning and personalized learning.
Additionally, deep learning is also being used in the education sector for employee engagement and resume analysis. Deep learning is being used in various industries to improve customer experience, enhance security, and optimize operations. It is being used in media and advertising for search advertising, social media advertising, and recommendation systems. Deep learning is also being used in the energy sector for demand forecasting and grid optimization. Deep learning is being used in various applications such as signal recognition, recommender systems, and lifestyle management. It is being used in the entertainment industry for music and video recommendation. Deep learning is also being used in the transportation industry for traffic prediction and route optimization. Deep learning is being used in various industries to improve efficiency, accuracy, and productivity. It is being used to analyze big data, identify patterns, and make predictions.
Market Scope |
|
Report Coverage |
Details |
Page number |
180 |
Base year |
2023 |
Historic period |
2018-2022 |
Forecast period |
2024-2028 |
Growth momentum & CAGR |
Accelerate at a CAGR of 26.06% |
Market growth 2024-2028 |
USD 10.85 billion |
Market structure |
Fragmented |
YoY growth 2023-2024(%) |
21.6 |
Regional analysis |
North America, Europe, APAC, South America, and Middle East and Africa |
Performing market contribution |
North America at 36% |
Key countries |
US, China, UK, Canada, and Germany |
Competitive landscape |
Leading Companies, Market Positioning of Companies, Competitive Strategies, and Industry Risks |
Key companies profiled |
Advanced Micro Devices Inc., Amazon.com Inc., Atomwise Inc., Comma.ai Inc., Deep Instinct, DeepMind Technologies Ltd., Graphcore Ltd., H2O.ai Inc., Hewlett Packard Enterprise Co., Intel Corp., International Business Machines Corp., Micron Technology Inc., Microsoft Corp., Mphasis Ltd., NVIDIA Corp., Qualcomm Inc., Samsung Electronics Co. Ltd., Sensory Inc., Teledyne FLIR LLC, and Viz.ai Inc. |
Market dynamics |
Parent market analysis, market growth inducers and obstacles, market forecast, fast-growing and slow-growing segment analysis, COVID-19 impact and recovery analysis and future consumer dynamics, market condition analysis for the forecast period |
Customization purview |
If our market report has not included the data that you are looking for, you can reach out to our analysts and get segments customized. |
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1 Executive Summary
2 Market Landscape
3 Market Sizing
4 Historic Market Size
5 Five Forces Analysis
6 Market Segmentation by Application
7 Market Segmentation by Type
8 Customer Landscape
9 Geographic Landscape
10 Drivers, Challenges, and Opportunity/Restraints
11 Competitive Landscape
12 Competitive Analysis
13 Appendix
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