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Deep learning听enables the system to be self-training to learn how to perform specific tasks. And AI itself is part of a larger area called cognitive computing. In ML, pruning means simplifying, compressing, and optimizing a decision tree by removing sections that are uncritical or redundant.
The global market for Deep Learning for Cognitive Computing was estimated to be worth US$ 32680 million in 2023 and is forecast to a readjusted size of US$ 69530 million by 2030 with a CAGR of 11.6% during the forecast period 2024-2030
The global deep learning for cognitive computing market refers to the market for deep learning technologies and solutions that are specifically applied in cognitive computing systems. Cognitive computing involves the development of systems that can mimic human intelligence, understand and interpret natural language, recognize patterns, make decisions, and learn from data.
Deep learning is a subset of machine learning that utilizes artificial neural networks with multiple layers to process and analyze large amounts of data. It allows cognitive computing systems to understand complex patterns, extract meaningful insights, and make accurate predictions or decisions.
The market for deep learning in cognitive computing is driven by several factors, including:
Advancements in AI and Machine Learning: The rapid advancements in AI and machine learning technologies have enabled the development of more sophisticated deep learning algorithms. These algorithms can process vast amounts of structured and unstructured data, leading to significant advancements in cognitive computing capabilities.
Big Data and IoT: The proliferation of big data and the ever-increasing number of connected devices through the Internet of Things (IoT) generate vast amounts of data. Deep learning provides the tools to analyze and extract valuable insights from this data, enabling more effective cognitive computing applications.
Natural Language Processing (NLP): Deep learning techniques, such as recurrent neural networks (RNNs) and long short-term memory (LSTM), have revolutionized natural language processing. This has led to significant progress in the development of conversational AI systems, chatbots, and virtual assistants that can understand and respond to human language.
Healthcare and Life Sciences: The healthcare and life sciences sector has witnessed substantial growth in the adoption of deep learning for cognitive computing applications. Deep learning algorithms can analyze medical images, genomics data, patient records, and clinical trials data to improve disease diagnosis, drug discovery, personalized medicine, and patient care.
Financial Services: Deep learning has also found extensive use in the financial services industry. It enables advanced fraud detection, algorithmic trading, risk assessment, credit scoring, and customer behavior analysis, improving operational efficiency and reducing financial risks.
Automotive and Manufacturing: The automotive and manufacturing sectors utilize deep learning in cognitive computing applications for autonomous vehicles, predictive maintenance, quality control, supply chain optimization, and robotics, among others. Deep learning enables these industries to leverage AI technologies for more efficient and intelligent operations.
North America has been a significant contributor to the global deep learning for cognitive computing market, primarily driven by extensive research and development activities, the presence of leading technology companies, and early adoption of AI technologies. However, the market is witnessing growth in other regions as well, including Europe, Asia Pacific, and Latin America, as organizations across various industries realize the potential benefits of deep learning in cognitive computing.
The market is highly competitive, with major technology companies, startups, and research institutions actively engaged in developing and commercializing deep learning solutions for cognitive computing. The key players in the market offer a wide range of deep learning frameworks, platforms, and tools to support cognitive computing applications.
In summary, the global deep learning for cognitive computing market is experiencing significant growth, fueled by advancements in AI and machine learning, the proliferation of big data and IoT, and the increasing adoption of deep learning in various industries. As organizations seek to harness the power of cognitive computing to gain insights from data and improve decision-making processes, the market for deep learning in cognitive computing is expected to expand further in the coming years.The global deep learning for cognitive computing market refers to the market for deep learning technologies and solutions that are specifically applied in cognitive computing systems. Cognitive computing involves the development of systems that can mimic human intelligence, understand and interpret natural language, recognize patterns, make decisions, and learn from data.
Deep learning is a subset of machine learning that utilizes artificial neural networks with multiple layers to process and analyze large amounts of data. It allows cognitive computing systems to understand complex patterns, extract meaningful insights, and make accurate predictions or decisions.
The market for deep learning in cognitive computing is driven by several factors, including:
Advancements in AI and Machine Learning: The rapid advancements in AI and machine learning technologies have enabled the development of more sophisticated deep learning algorithms. These algorithms can process vast amounts of structured and unstructured data, leading to significant advancements in cognitive computing capabilities.
Big Data and IoT: The proliferation of big data and the ever-increasing number of connected devices through the Internet of Things (IoT) generate vast amounts of data. Deep learning provides the tools to analyze and extract valuable insights from this data, enabling more effective cognitive computing applications.
Natural Language Processing (NLP): Deep learning techniques, such as recurrent neural networks (RNNs) and long short-term memory (LSTM), have revolutionized natural language processing. This has led to significant progress in the development of conversational AI systems, chatbots, and virtual assistants that can understand and respond to human language.
Healthcare and Life Sciences: The healthcare and life sciences sector has witnessed substantial growth in the adoption of deep learning for cognitive computing applications. Deep learning algorithms can analyze medical images, genomics data, patient records, and clinical trials data to improve disease diagnosis, drug discovery, personalized medicine, and patient care.
Financial Services: Deep learning has also found extensive use in the financial services industry. It enables advanced fraud detection, algorithmic trading, risk assessment, credit scoring, and customer behavior analysis, improving operational efficiency and reducing financial risks.
Automotive and Manufacturing: The automotive and manufacturing sectors utilize deep learning in cognitive computing applications for autonomous vehicles, predictive maintenance, quality control, supply chain optimization, and robotics, among others. Deep learning enables these industries to leverage AI technologies for more efficient and intelligent operations.
North America has been a significant contributor to the global deep learning for cognitive computing market, primarily driven by extensive research and development activities, the presence of leading technology companies, and early adoption of AI technologies. However, the market is witnessing growth in other regions as well, including Europe, Asia Pacific, and Latin America, as organizations across various industries realize the potential benefits of deep learning in cognitive computing.
The market is highly competitive, with major technology companies, startups, and research institutions actively engaged in developing and commercializing deep learning solutions for cognitive computing. The key players in the market offer a wide range of deep learning frameworks, platforms, and tools to support cognitive computing applications.
In summary, the global deep learning for cognitive computing market is experiencing significant growth, fueled by advancements in AI and machine learning, the proliferation of big data and IoT, and the increasing adoption of deep learning in various industries. As organizations seek to harness the power of cognitive computing to gain insights from data and improve decision-making processes, the market for deep learning in cognitive computing is expected to expand further in the coming years.
Report Scope
This report aims to provide a comprehensive presentation of the global market for Deep Learning for Cognitive Computing, focusing on the total sales revenue, key companies market share and ranking, together with an analysis of Deep Learning for Cognitive Computing by region & country, by Type, and by Application.
The Deep Learning for Cognitive Computing market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. With both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Deep Learning for Cognitive Computing.
Market Segmentation
By Company
Microsoft
IBM
SAS Institute
Amazon Web Services
CognitiveScale
Numenta
Expert .AI
Cisco
Google LLC
Tata Consultancy Services
Infosys Limited
BurstIQ Inc
Red Skios
e-Zest Solutions
Vantage Labs
Cognitive Software Group
SparkCognition
Segment by Type:
Platform
Services
Segment by Application
Intelligent Automation
Intelligent Virtual Assistants and Chatbots
Behavior Analysis
Biometrics
By Region
North America
United States
Canada
Europe
Germany
France
U.K.
Italy
Russia
Asia-Pacific
China
Japan
South Korea
China Taiwan
Southeast Asia
India
Latin America
Mexico
Brazil
Argentina
Middle East & Africa
Turkey
Saudi Arabia
UAE
Chapter Outline
Chapter 1: Introduces the report scope of the report, global total market size. This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.
Chapter 2: Detailed analysis of Deep Learning for Cognitive Computing manufacturers competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 3: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 5: Revenue of Deep Learning for Cognitive Computing in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.
Chapter 6: Revenue of Deep Learning for Cognitive Computing in country level. It provides sigmate data by Type, and by Application for each country/region.
Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.
Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.
Chapter 9: Conclusion.

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