The Deep Learning Market size was valued at USD 45.82 billion in 2022. It is projected to reach USD 638.37 billion by 2031, growing at a CAGR of 34% during the forecast period (2023-2031).
Deep learning, considered a subfield of Machine Learning, is concerned with algorithms and is largely inspired by the brain's structure and a function called the artificial neural network. Technology is advancing at an alarming pace, and the latest advancements in artificial intelligence (AI) are nothing short of overwhelming. Deep learning is gaining momentum in AI owing to its supremacy in terms of accuracy when trained with large volumes of data. The current era deals with big data, which is estimated to provide significant opportunities for new innovations in deep learning. Deep learning needs high-end machines, contrary to traditional machine learning algorithms. In addition, in traditional machine learning techniques, a majority of the applied features need to be identified by a domain expert in order to reduce data complexity and make patterns more visible for learning algorithms to work. However, deep learning learns high-level features from data in an incremental manner, which eliminates the requirement for domain expertise as well as hardcore feature extraction. Deep learning is also known as deep neural learning or deep neural network.
Nowadays, big data is extensively adopted by various business organizations as they are collecting a significant amount of data according to the requirements of the organization. This data generation is estimated to increase further with upcoming technologies such as 5G. Therefore, it is estimated that deep learning will find applications in big data analytics in order to extract sophisticated patterns from a large amount of data. Deep learning can learn and assess a significant amount of unsupervised data; hence, it is considered an appropriate tool for big data analytics. The increasing demand for big data analytics is estimated to further foster the deep learning market growth.
Deep learning has applications in chatbots, machine translation, and service bots. A trained Deep Neural Network (DNN) translates a sentence or a word without the use of a large database. DNNs produce more accurate and better results than traditional machine translation techniques, which improves system performance. Deep learning algorithms can be utilized in chatbots and service bots to enhance customer service and reduce call center workload. Deep learning platform application in chatbots includes Automatic Speech Recognition (ASR) to convert audio into text and Natural-language processing (NLP) for an automated call transfer.
Deep learning requires a large amount of data, as well as a high initial investment, in order to outperform other techniques. Because of the complexity of the data models, training is extremely expensive. Furthermore, deep learning necessitates the use of expensive GPUs and hundreds of machines. As a result, the initial cost is increased in order to obtain more accurate results with maximum precision.
Nowadays, competition in sectors is intensifying, and players are implementing various strategies to understand customer behavior. Customized products and services are gaining traction today; hence, companies are adopting artificial intelligence to collect and handle data regarding customer requirements and preferences. By doing so, they are able to apply tailor-made offerings and provide a personalized shopping experience. Online shopping sites and social media platforms also provide customized notifications for each and every user. AI-enabled with deep learning can analyze massive volumes of customer data within seconds. It also provides insights regarding previous shopping history and analyses customer choices. Through these techniques, players can also understand customers' price preferences. The escalating use of artificial intelligence in customer data analysis is anticipated to create ample opportunities for the global deep-learning market.
The global deep learning market is segmented by solution, hardware, application, and end-user.
The global market is bifurcated into hardware, software, and services.
The software segment is the highest contributor to the market and is expected to grow at a CAGR of xx% during the forecast period, owing to the rising adoption of the Software as a Service model due to its cost-effectiveness and user-friendliness. As a result, businesses are working on deep learning frameworks that will aid in the design, training, and validation of deep neural networks using a greater standard of programming, advanced tools, and libraries. Furthermore, machine comprehension, ONNX architecture, and edge intelligence improve the deep learning capacity throughout organizations.
The hardware segment is expected to witness a higher CAGR. Several startups and established businesses are focusing on new hardware developments to facilitate smooth deep-learning processing. Several organizations are working on deep learning chipsets and hardware to accelerate the growth of deep learning technology.
The global market is bifurcated into CPU, GPU, FPGA, and ASIC.
The GPU segment is the highest contributor to the market and is expected to grow at a CAGR of xx% during the forecast period. Because of their high memory bandwidth and throughput, GPUs are broadly used hardware for enhancing learning and classification procedures in Computer Neural Networks (CNNs). The GPU improves computational capability, allowing the system to run multiple parallel processes. Multi-GPU improves deep learning performance and accuracy of multiple GPUs in a single computer. Furthermore, it has the ability to perform a wide range of tasks concurrently and accurately in real time.
The field programmable gate array (FPGA) segment is expected to grow at a significant rate. FPGA setups were once used only for training, but now they are widespread in a variety of applications. FPGA is adaptable, fast, and power-efficient, making it ideal for processing data in data centers. Furthermore, FPGAs have gained popularity among researchers and engineers because they allow for the rapid prototyping of multiple designs in significantly less time than a traditional IC.
The global market is bifurcated into image recognition, voice recognition, video surveillance & diagnostics, and data mining.
The image recognition segment is the highest contributor to the market and is expected to grow at a CAGR of xx% during the forecast period. Deep learning can be utilized in stock video and photography websites to help users discover visual content. The technology can be used in image search, allowing users to look for similar images or products by referencing an image. The growing visual content on social media, as well as the need for content modernization, will drive the use of image recognition.
The data mining segment is expected to grow at a significant rate. Deep learning can address issues that arise throughout data mining and extraction procedures, such as rapidly streaming data, data analysis trustworthiness, imbalanced input data, and widely dispersed input sources. A deep-learning algorithm performs several tasks like tagging videos, semantic indexing, text, and images.
The global market is bifurcated into automotive, aerospace & defense, healthcare, manufacturing, and marketing.
The automotive segment is the highest contributor to the market and is expected to grow at a CAGR of xx% during the forecast period. The autonomous vehicle is a new innovation that necessitates massive computing power. A deep neural network can quickly assist an autonomous vehicle in carrying out various tasks without human intervention. Autonomous vehicles are anticipated to gain traction in the coming years, and as a result, numerous startups and large corporations are continuing to work on their development.
The healthcare segment is expected to grow at a significant rate. The healthcare industry's digital transformation is anticipated to continue in the coming years, offering an opportunity for innovative technologies such as deep learning to intervene. Deep learning can be employed in predictive analytics to detect diseases earlier, identify medical risks and their drivers, and accurately predict hospitalization. Several governments worldwide have taken steps to integrate AI and deep learning in healthcare will drive the market during the forecast period.
The global deep learning market is bifurcated into four regions, namely North America, Europe, Asia-Pacific, and LAMEA.
North America region is the highest contributor to the market and is expected to grow at a CAGR of xx% during the forecast owing to the increasing demand for deep learning applications, including image recognition, data mining, and signal recognition. Deep learning has paved the way for significant improvements in image recognition in terms of accuracy. Key players in the region are increasing investments in deep learning technology. The availability of an established IT environment and high investments are expected to drive market growth in North America. For example, the United States Defense Advanced Research Projects Agency (DARPA) invested USD 2 billion in AI technology development. Furthermore, the region is an early adopter of advanced technologies, which broadens the adoption of deep learning at a faster pace.
Europe is expected to witness dynamic growth in the deep learning market during the forecast period, as several new measures have been implemented to help the region's artificial intelligence sector stimulate growth and deliver a digital economy. As a result, there have been numerous opportunities for growth in deep learning technology. The United Kingdom is laying the groundwork for technology to advance further in the field of autonomous vehicles, cyber security, and smart devices. A 10.4 billion USD program called "Digital Europe" was proposed by the European Union for the years 2021 to 2027. The program aims to develop AI technology and spread its uses throughout society and the economy. Such proactive actions are probably going to open up new market opportunities and boost the market growth in Europe.
Asia-Pacific is expected to witness significant growth in the deep learning market during the forecast period. China, India, and the Philippines, three of the region's developing economies, offer a dynamic and strong startup ecosystem supported by a growingly skilled labor force that fuels regional market expansion. Along with the rapid uptake of machine learning services, which is a key factor in driving the market in Japan, the Japanese government is also undertaking numerous initiatives to promote artificial intelligence throughout the nation. These are a few of the elements that stimulate market expansion in Asia and the Pacific.
The LAMEA region is expected to witness moderate growth in the global deep-learning market during the forecast period. Artificial intelligence is being actively used by the oil-rich Gulf States to diversify their economies. The majority of Gulf nations are constantly concentrating on the creation of new technologies because they recognize the importance of advanced technology. UAE is at the forefront of technological innovation and adoption in the Arab world. The demand for Ai technologies in the region is also driven by smart city initiatives and autonomous transportation. To increase regional adoption of advanced technologies, South American nations like Brazil, Mexico, and Uruguay are creating new AI policies and cogent strategies. In the future, the area is anticipated to offer fresh, lucrative market opportunities.
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