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1468063

2024-2032 年按產品類型、應用、最終用途產業、架構和地區分類的深度學習市場報告

Deep Learning Market Report by Product Type, Application, End-Use Industry, Architecture, and Region 2024-2032
出版日期: | 出版商: IMARC | 英文 148 Pages | 商品交期: 2-3個工作天內

價格

IMARC Group年全球深度學習市場規模達235億美元。人工智慧（AI）的日益普及、資料處理的進步、對圖像和語音識別的需求不斷成長、研發投資以及巨量資料和雲端運算技術的引入是推動人工智慧發展的主要因素。

深度學習是人工智慧 (AI) 的一個子領域，涉及訓練人工神經網路以從大量資料中學習並做出決策。這些神經網路由互連的節點層組成，模仿人腦的結構，網路迭代地調整其內部參數以識別資料中的模式、特徵和表示，使它們能夠識別物件、理解語音、翻譯語言和甚至玩策略遊戲。它還改變了多個領域，包括電腦視覺、自然語言處理 (NLP) 和機器人技術，在以前被認為對傳統機器學習方法具有挑戰性的任務中實現了顯著突破。

該市場主要受到資訊科技（IT）產業大幅擴張的推動。此外，數位化趨勢的不斷發展，以及深度學習自動提取原始資料的廣泛採用，使其成為高精度、高效率解決複雜現實問題的強大工具，正在影響市場成長。它還透過自動分析可用資料來處理資料，從而實現更有效率、更準確的決策。此外，網路安全、詐欺檢測、醫學影像分析和醫療保健虛擬患者援助等服務的廣泛使用是另一個主要的成長誘導因素。除此之外，巨量資料分析和雲端運算的整合以及持續改進硬體和軟體處理的研發（R＆D）努力正在進一步加速市場成長。此外，這些技術提供的可擴展性和運算能力使組織能夠有效地處理和分析大量資料集，從而創造積極的市場前景。

深度學習市場趨勢/促進因素：

影像和語音辨識對深度學習的需求不斷成長

分析和識別圖像中的模式、物件和特徵的需求不斷成長，推動了市場的成長。此外，由深度學習驅動的醫學影像系統有助於診斷疾病、檢測異常並協助醫療保健領域的手術計劃，從而影響市場成長。此外，在自動駕駛汽車中，影像辨識可以即時識別交通標誌、行人和障礙物，提高自動駕駛汽車的安全性和效率，這是另一個主要的成長因素。除此之外，語音辨識對於自然語言處理（NLP）應用程式和語音助理的開發至關重要。此外，還採用深度學習模型將語音轉錄為文本，使包括 Siri、Alexa 和 Google Assistant 在內的語音控制虛擬助理能夠準確理解和回應使用者命令。這改變了人們與科技互動的方式，並實現了免持和直覺的使用者體驗。此外，客戶服務中心、呼叫中心和語言翻譯服務中語音辨識產品的採用正在簡化溝通並縮短回應時間，從而推動市場成長。

不斷增加的研發投資（R&D）

深度學習持續快速發展，各行業的組織都在分配大量資源來增強這項尖端技術的功能和應用。此外，研發投資專注於學習的各個方面以及新穎演算法和架構的開發，以提高性能、準確性和效率，從而影響市場成長。此外，研究人員正在不斷探索注意力機制、變壓器和生成對抗網路（GAN）等創新技術，以在自然語言處理、電腦視覺和其他人工智慧驅動的任務方面取得突破。此外，硬體最佳化也是研發投入的另一個重點。組織正在開發專用處理器，例如圖形處理單元 (GPU) 和張量處理單元 (TPU)，旨在加速深度學習運算。這些硬體進步可縮短訓練時間和推理速度，使模型更易於企業存取和擴展。

落實政府有利舉措

政府的支持和措施對於促進市場成長至關重要。此外，各國政府正認知到人工智慧（AI）的變革潛力，並積極投資人工智慧研發項目，促進研究、開發，進而影響市場成長。此外，政府機構的金融投資使大學、研究機構和私人公司能夠開展雄心勃勃的深度學習項目，這些項目突破了創新的界限，推動了技術進步，這是另一個主要的成長誘導因素。除此之外，政府經常建立以人工智慧為中心的卓越中心和創新中心，作為研究人員、學者和產業專家的協作空間，促進知識共享、網路和跨學科研究，營造有利於深度學習突破性發現的環境。此外，各國政府積極參與公私合作，以加速跨產業的產品採用，並制定鼓勵負責任的人工智慧開發和部署的政策和法規，從而推動市場成長。

目錄

第1章：前言

第 2 章：範圍與方法

  • 研究目的
  • 利害關係人
  • 資料來源
    • 主要資源
    • 二手資料
  • 市場預測
    • 自下而上的方法
    • 自上而下的方法
  • 預測方法

第 3 章：執行摘要

第 4 章：簡介

  • 概述
  • 主要行業趨勢

第 5 章：全球深度學習市場

  • 市場概況
  • 市場業績
  • COVID-19 的影響
  • 市場預測

第 6 章：市場區隔：依產品類型

  • 軟體
    • 市場走向
    • 市場預測
  • 服務
    • 市場走向
    • 市場預測
  • 硬體
    • 市場走向
    • 市場預測

第 7 章：市場區隔：按應用

  • 影像辨識
    • 市場走向
    • 市場預測
  • 訊號識別
    • 市場走向
    • 市場預測
  • 資料探勘
    • 市場走向
    • 市場預測
  • 其他
    • 市場走向
    • 市場預測

第 8 章：市場區隔：依最終用途產業

  • 安全
    • 市場走向
    • 市場預測
  • 製造業
    • 市場走向
    • 市場預測
  • 零售
    • 市場走向
    • 市場預測
  • 汽車
    • 市場走向
    • 市場預測
  • 衛生保健
    • 市場走向
    • 市場預測
  • 農業
    • 市場走向
    • 市場預測
  • 其他
    • 市場走向
    • 市場預測

第 9 章：市場區隔：依架構

  • 循環神經網路
    • 市場走向
    • 市場預測
  • CNN
    • 市場走向
    • 市場預測
  • 資料庫網路
    • 市場走向
    • 市場預測
  • 數據服務網路
    • 市場走向
    • 市場預測
  • 格魯烏
    • 市場走向
    • 市場預測

第 10 章：市場區隔：按地區

  • 北美洲
    • 美國
    • 加拿大
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 其他
  • 歐洲
    • 德國
    • 法國
    • 英國
    • 義大利
    • 西班牙
    • 俄羅斯
    • 其他
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 其他
  • 中東和非洲
    • 市場走向
    • 市場細分：按國家/地區
    • 市場預測

第 11 章：SWOT 分析

  • 概述
  • 優勢
  • 弱點
  • 機會
  • 威脅

第 12 章：價值鏈分析

第 13 章：波特五力分析

  • 概述
  • 買家的議價能力
  • 供應商的議價能力
  • 競爭程度
  • 新進入者的威脅
  • 替代品的威脅

第14章：競爭格局

  • 市場結構
  • 關鍵參與者
  • 關鍵參與者簡介
    • Amazon Web Services (AWS)
    • Google Inc.
    • IBM
    • Intel
    • Micron Technology
    • Microsoft Corporation
    • Nvidia
    • Qualcomm
    • Samsung Electronics
    • Sensory Inc.
    • Pathmind Inc.
    • Xilinx
Product Code: SR112024A1941

The global deep learning market size reached US$ 23.5 Billion in 2023. Looking forward, IMARC Group expects the market to reach US$ 295.1 Billion by 2032, exhibiting a growth rate (CAGR) of 31.5% during 2024-2032. The increasing artificial intelligence (AI) adoption, advancements in data processing, the growing demand for image and speech recognition, investments in research and development (R&D), and the introduction of big data and cloud computing technologies are some of the major factors propelling the market.

Deep learning is a subfield of artificial intelligence (AI) that involves training artificial neural networks to learn and make decisions from vast amounts of data. These neural networks consist of interconnected layers of nodes, mimicking the structure of the human brain, the networks iteratively adjust their internal parameters to identify patterns, features, and representations within the data, allowing them to recognize objects, comprehend speech, translate languages, and even play strategic games. It also transforms various domains, including computer vision, natural language processing (NLP), and robotics, achieving remarkable breakthroughs in tasks previously considered challenging for traditional machine learning approaches.

The market is primarily driven by the significant expansion of the information technology (IT) industry. In addition, the growing trend of digitalization, and the widespread adoption of deep learning for automatically extracting raw data, making it a powerful tool for solving complex real-world problems with high accuracy and efficiency, is influencing market growth. It also processes data by automatically analyzing available data, resulting in more efficient and accurate decision-making. Moreover, the extensive service use of in cybersecurity, fraud detection, medical image analysis, and virtual patient assistance in healthcare represents another major growth-inducing factor. Besides this, the integration of big data analytics and cloud computing and ongoing research and development (R&D) efforts to improve hardware and software processing are further accelerating the market growth. Furthermore, the scalability and computational power offered by these technologies allow organizations to process and analyze vast datasets efficiently, thus creating a positive market outlook.

Deep Learning Market Trends/Drivers:

The rising demand for deep learning for image and speech recognition

The growing demand to analyze and identify patterns, objects, and features within images is escalating the market growth. Additionally, deep learning-powered medical imaging systems assist in diagnosing diseases, detecting anomalies, and assisting in surgical planning in the healthcare sector thus influencing the market growth. Moreover, in autonomous vehicles image recognition enables real-time identification of traffic signs, pedestrians, and obstacles, enhancing the safety and efficiency of self-driving cars, representing another major growth-inducing factor. Besides this, speech recognition is essential in the development of natural language processing (NLP) applications and voice assistants. Also, deep learning models are employed to transcribe speech into text, enabling voice-controlled virtual assistants including Siri, Alexa, and Google Assistant to understand and respond to user commands accurately. This has transformed the way people interact with technology and enabled hands-free and intuitive user experiences. Furthermore, the product adoption of for speech recognition in customer service centers, call centers, and language translation services is streamlining communication and improving response times thus propelling the market growth.

The increasing investment in research and development (R&D)

Deep learning continues to evolve rapidly, and organizations across industries are allocating substantial resources to enhance the capabilities and applications of this cutting-edge technology. Additionally, the investments in R&D focus on various aspects of learning and the development of novel algorithms and architectures that improve performance, accuracy, and efficiency, thus influencing market growth. Also, researchers are continuously exploring innovative techniques such as attention mechanisms, transformers, and generative adversarial networks (GANs) to achieve breakthroughs in natural language processing, computer vision, and other AI-driven tasks. Moreover, hardware optimization is another focal point of R&D investments. Organizations are developing specialized processors, such as graphical processing units (GPUs) and tensor processing units (TPUs), designed to accelerate deep learning computations. These hardware advancements enable faster training times and inference, making the models more accessible and scalable for businesses.

The implementation of favorable government initiatives

Government support and initiatives are essential in fostering the market growth. Additionally, governments are recognizing the transformative potential of artificial intelligence (AI), and actively investing AI research and development projects, and promoting research, development, thus influencing market growth. Moreover, financial investments from government agencies allow universities, research institutions, and private companies to undertake ambitious deep-learning projects that push the boundaries of innovation and drive technological advancements representing another major growth-inducing factor. Besides this, governments often establish AI-focused centers of excellence and innovation hubs that serve as collaborative spaces for researchers, academics, and industry experts which facilitate knowledge sharing, networking, and interdisciplinary research, fostering an environment conducive to breakthrough discoveries in deep learning. Furthermore, governments actively engage in public-private partnerships to accelerate the product adoption across industries and create policies and regulations that encourage responsible AI development and deployment thus propelling the market growth.

Deep Learning Industry Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the global deep learning market report, along with forecasts at the global, regional and country levels from 2024-2032. Our report has categorized the market based on product type, application, end-use industry and architecture.

Breakup by Product Type:

Software

Services

Hardware

Software represents the most popular product type

The report has provided a detailed breakup and analysis of the market based on the product type. This includes software, services, and hardware. According to the report, software accounted for the largest market share.

Software is essential in the development and implementation of deep learning algorithms and models. It provides the necessary tools and frameworks for researchers, data scientists, and developers to create and train complex neural networks efficiently. As a result, software solutions have become indispensable for unlocking the full potential of technology. Moreover, the flexibility and scalability offered by the software make it highly attractive to businesses across various industries. Software-based solutions allow organizations to integrate deep learning capabilities into their existing systems and applications seamlessly empowering businesses to use the power of AI-driven insights and automation to optimize processes, improve decision-making, and enhance customer experiences.

Besides this, the open-source nature of many software platforms fosters collaboration and knowledge sharing within the AI community. Popular open-source libraries such as TensorFlow and PyTorch are essential in democratizing access to technology, enabling widespread adoption and innovation. Furthermore, the continuous advancements in software, driven by ongoing research and development, are resulting in improved performance and efficiency.

Breakup by Application:

Image Recognition

Signal Recognition

Data Mining

Others

Image recognition represents the most popular application segment

The report has provided a detailed breakup and analysis of the market based on the application. This includes image recognition, signal recognition, data mining, and others. According to the report, image recognition accounted for the largest market share.

Image recognition is currently dominating the market growth due to its wide-ranging applications and transformative impact across various industries. They are demonstrating exceptional capabilities in accurately identifying and analyzing objects, patterns, and features within images, making them highly sought after for diverse use cases. Moreover, deep learning-powered medical imaging systems aid in the early detection of diseases, assist in precise diagnoses, and support treatment planning in the healthcare industry.

Besides this, in the automotive sector, image recognition is essential for enabling advanced driver assistance systems (ADAS) and autonomous vehicles, enhancing safety and efficiency on the roads, thus accelerating the market growth. Moreover, the retail and e-commerce sectors use image recognition for visual search, product recommendation, and inventory management that enhances customer experiences, streamlines operations, and drives sales.

Breakup by End-Use Industry:

Security

Manufacturing

Retail

Automotive

Healthcare

Agriculture

Others

Security holds the largest share of the market

A detailed breakup and analysis of the market based on the end use industry has also been provided in the report. This includes security, manufacturing, retail, automotive, healthcare, agriculture, and others. According to the report, security accounted for the largest market share.

Deep learning technology offers unprecedented capabilities in detecting, analyzing, and responding to complex security breaches and attacks. In addition, the increasing demand for robust and advanced solutions to combat the ever-evolving landscape of cyber threats, is influencing the market growth. In the cybersecurity domain, deep learning algorithms excel in anomaly detection, identifying suspicious patterns and activities that traditional security systems may miss.

Moreover, the growing demand for cutting-edge security measures, such as deep learning-powered intrusion detection systems, malware detection, and behavioral analytics to offer organizations with enhanced defense mechanisms against emerging threats represents another major growth-inducing factor. Additionally, the vast amounts of data generated in the cybersecurity landscape require advanced data processing and analysis capabilities. It excels in handling big data and efficiently extracting meaningful insights, enabling security teams to make informed decisions and respond proactively to potential threats.

Breakup by Architecture:

RNN

CNN

DBN

DSN

GRU

A detailed breakup and analysis of the market based on the architecture has also been provided in the report. This includes RNN, CNN, DBN, DSN, and GRU.

Recurrent neural networks (RNN) are designed to handle sequential data, such as time series or natural language. Their recurrent nature allows them to capture temporal dependencies within the data. RNNs have internal memory that enables them to process sequences of variable length, making them ideal for tasks such as language modeling, machine translation, and sentiment analysis.

Moreover, convolutional neural networks (CNN) are employed for image and video processing tasks as they excel at feature extraction through convolutional layers, which scan input data with small filters to identify patterns and spatial relationships. CNNs are widely employed in image recognition, object detection, and image classification tasks due to their ability to automatically learn relevant visual features. Besides this, deep belief networks (DBN) are generative models that consist of multiple layers of stochastic, latent variables, used in unsupervised learning tasks, such as feature learning and dimensionality reduction, making them useful in applications such as speech recognition and recommendation systems.

Apart from this, deep stacking networks (DSN) are a type of autoencoder-based architecture used for unsupervised feature learning involving multiple stacked layers that progressively learn to encode and decode data representations which find applications in anomaly detection, data compression, and denoising tasks. Furthermore, gated recurrent units (GRU) are a variant of RNNs that aim to address the vanishing gradient problem and improve training efficiency which use gating mechanisms to regulate the flow of information through the network, allowing them to retain essential information for longer sequences and avoid long-term dependencies issues.

Breakup by Region:

North America

United States

Canada

Asia Pacific

China

Japan

India

South Korea

Australia

Indonesia

Others

Europe

Germany

France

United Kingdom

Italy

Spain

Russia

Others

Latin America

Brazil

Mexico

Others

Middle East and Africa

North America exhibits a clear dominance in the market

The report has also provided a comprehensive analysis of all the major regional markets, which include North America (the United States and Canada); Europe (Germany, France, the United Kingdom, Italy, Spain, Russia, and others); Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, and others); Latin America (Brazil, Mexico, and others); and the Middle East and Africa. According to the report, North America accounted for the largest market share.

North America is home to some of the world's leading tech giants, research institutions, and AI startups, which heavily invest in research and development (R&D) for advanced technology. The presence of these industry leaders fosters a competitive ecosystem, driving advancements in algorithms, hardware, and software. Moreover, the highly skilled workforce comprising AI experts, data scientists, and engineers, is contributing to the development of sophisticated models and applications thus representing another major growth-inducing factor.

Besides this, North America's strong emphasis on entrepreneurship and venture capital funding allows the growth of AI-driven startups that often pioneer groundbreaking applications, further propelling market expansion. Additionally, supportive government policies, such as tax incentives and funding for AI research, encourage innovation, and attract businesses and investments to the region. Furthermore, the well-established infrastructure, including robust cloud computing services and high-performance computing resources, facilitates the scalability and deployment of complex deep learning models across the region.

Competitive Landscape:

At present, key players in the market are adopting various strategies to strengthen their position and gain a competitive edge. Companies are investing heavily in research and development (R&D) to stay at the forefront of deep learning technology focusing on improving algorithms, developing novel architectures, and exploring new applications to offer cutting-edge solutions to their customers. Moreover, several companies are engaging in strategic acquisitions and partnerships to expand their offerings and capabilities. Key players are expanding their operations to new geographic regions to tap into emerging markets and reach a broader customer base, including establishing regional offices, forming partnerships with local companies, and adapting their offerings to suit regional needs. They are providing excellent customer support and training services for customer satisfaction and loyalty and investing in customer support teams and educational resources to ensure their clients can maximize the value of their solutions.

The report has provided a comprehensive analysis of the competitive landscape in the market. Detailed profiles of all major companies have also been provided. Some of the key players in the market include:

Amazon Web Services (AWS)

Google Inc.

IBM

Intel

Micron Technology

Microsoft Corporation

Nvidia

Qualcomm

Samsung Electronics

Sensory Inc.,

Pathmind, Inc.

Xilinx

Recent Developments:

In October 2020, NVIDIA AI and Microsoft Azure team collaborated to improve the AI-powered grammar checker in Microsoft word which can now tap into the NVIDIA triton inference server, ONNX Runtime, and Microsoft Azure machine learning (ML) to provide this smart experience.

In May 2022, Intel introduced its second-generation Habana AI deep learning processors in order to deliver high efficiency and high performance. Intel is executing its AI strategy to give customers numerous solution choices from the cloud to the edge, addressing the increasing number and complex nature of AI workloads.

In August 2022, Amazon web services introduced a new machine learning (ML) software through which medical records of patients can be analyzed for better treatment of patients and reduce expenses.

Key Questions Answered in This Report

  • 1. What was the size of the global deep learning market in 2023?
  • 2. What is the expected growth rate of the global deep learning market during 2024-2032?
  • 3. What has been the impact of COVID-19 on the global deep learning market?
  • 4. What are the key factors driving the global deep learning market?
  • 5. What is the breakup of the global deep learning market based on the product type?
  • 6. What is the breakup of the global deep learning market based on the application?
  • 7. What is the breakup of the global deep learning market based on the end-use industry?
  • 8. What are the key regions in the global deep learning market?
  • 9. Who are the key players/companies in the global deep learning market?

Table of Contents

1 Preface

2 Scope and Methodology

  • 2.1 Objectives of the Study
  • 2.2 Stakeholders
  • 2.3 Data Sources
    • 2.3.1 Primary Sources
    • 2.3.2 Secondary Sources
  • 2.4 Market Estimation
    • 2.4.1 Bottom-Up Approach
    • 2.4.2 Top-Down Approach
  • 2.5 Forecasting Methodology

3 Executive Summary

4 Introduction

  • 4.1 Overview
  • 4.2 Key Industry Trends

5 Global Deep Learning Market

  • 5.1 Market Overview
  • 5.2 Market Performance
  • 5.3 Impact of COVID-19
  • 5.4 Market Forecast

6 Market Breakup by Product Type

  • 6.1 Software
    • 6.1.1 Market Trends
    • 6.1.2 Market Forecast
  • 6.2 Services
    • 6.2.1 Market Trends
    • 6.2.2 Market Forecast
  • 6.3 Hardware
    • 6.3.1 Market Trends
    • 6.3.2 Market Forecast

7 Market Breakup by Application

  • 7.1 Image Recognition
    • 7.1.1 Market Trends
    • 7.1.2 Market Forecast
  • 7.2 Signal Recognition
    • 7.2.1 Market Trends
    • 7.2.2 Market Forecast
  • 7.3 Data Mining
    • 7.3.1 Market Trends
    • 7.3.2 Market Forecast
  • 7.4 Others
    • 7.4.1 Market Trends
    • 7.4.2 Market Forecast

8 Market Breakup by End-Use Industry

  • 8.1 Security
    • 8.1.1 Market Trends
    • 8.1.2 Market Forecast
  • 8.2 Manufacturing
    • 8.2.1 Market Trends
    • 8.2.2 Market Forecast
  • 8.3 Retail
    • 8.3.1 Market Trends
    • 8.3.2 Market Forecast
  • 8.4 Automotive
    • 8.4.1 Market Trends
    • 8.4.2 Market Forecast
  • 8.5 Healthcare
    • 8.5.1 Market Trends
    • 8.5.2 Market Forecast
  • 8.6 Agriculture
    • 8.6.1 Market Trends
    • 8.6.2 Market Forecast
  • 8.7 Others
    • 8.7.1 Market Trends
    • 8.7.2 Market Forecast

9 Market Breakup by Architecture

  • 9.1 RNN
    • 9.1.1 Market Trends
    • 9.1.2 Market Forecast
  • 9.2 CNN
    • 9.2.1 Market Trends
    • 9.2.2 Market Forecast
  • 9.3 DBN
    • 9.3.1 Market Trends
    • 9.3.2 Market Forecast
  • 9.4 DSN
    • 9.4.1 Market Trends
    • 9.4.2 Market Forecast
  • 9.5 GRU
    • 9.5.1 Market Trends
    • 9.5.2 Market Forecast

10 Market Breakup by Region

  • 10.1 North America
    • 10.1.1 United States
      • 10.1.1.1 Market Trends
      • 10.1.1.2 Market Forecast
    • 10.1.2 Canada
      • 10.1.2.1 Market Trends
      • 10.1.2.2 Market Forecast
  • 10.2 Asia Pacific
    • 10.2.1 China
      • 10.2.1.1 Market Trends
      • 10.2.1.2 Market Forecast
    • 10.2.2 Japan
      • 10.2.2.1 Market Trends
      • 10.2.2.2 Market Forecast
    • 10.2.3 India
      • 10.2.3.1 Market Trends
      • 10.2.3.2 Market Forecast
    • 10.2.4 South Korea
      • 10.2.4.1 Market Trends
      • 10.2.4.2 Market Forecast
    • 10.2.5 Australia
      • 10.2.5.1 Market Trends
      • 10.2.5.2 Market Forecast
    • 10.2.6 Indonesia
      • 10.2.6.1 Market Trends
      • 10.2.6.2 Market Forecast
    • 10.2.7 Others
      • 10.2.7.1 Market Trends
      • 10.2.7.2 Market Forecast
  • 10.3 Europe
    • 10.3.1 Germany
      • 10.3.1.1 Market Trends
      • 10.3.1.2 Market Forecast
    • 10.3.2 France
      • 10.3.2.1 Market Trends
      • 10.3.2.2 Market Forecast
    • 10.3.3 United Kingdom
      • 10.3.3.1 Market Trends
      • 10.3.3.2 Market Forecast
    • 10.3.4 Italy
      • 10.3.4.1 Market Trends
      • 10.3.4.2 Market Forecast
    • 10.3.5 Spain
      • 10.3.5.1 Market Trends
      • 10.3.5.2 Market Forecast
    • 10.3.6 Russia
      • 10.3.6.1 Market Trends
      • 10.3.6.2 Market Forecast
    • 10.3.7 Others
      • 10.3.7.1 Market Trends
      • 10.3.7.2 Market Forecast
  • 10.4 Latin America
    • 10.4.1 Brazil
      • 10.4.1.1 Market Trends
      • 10.4.1.2 Market Forecast
    • 10.4.2 Mexico
      • 10.4.2.1 Market Trends
      • 10.4.2.2 Market Forecast
    • 10.4.3 Others
      • 10.4.3.1 Market Trends
      • 10.4.3.2 Market Forecast
  • 10.5 Middle East and Africa
    • 10.5.1 Market Trends
    • 10.5.2 Market Breakup by Country
    • 10.5.3 Market Forecast

11 SWOT Analysis

  • 11.1 Overview
  • 11.2 Strengths
  • 11.3 Weaknesses
  • 11.4 Opportunities
  • 11.5 Threats

12 Value Chain Analysis

13 Porters Five Forces Analysis

  • 13.1 Overview
  • 13.2 Bargaining Power of Buyers
  • 13.3 Bargaining Power of Suppliers
  • 13.4 Degree of Competition
  • 13.5 Threat of New Entrants
  • 13.6 Threat of Substitutes

14 Competitive Landscape

  • 14.1 Market Structure
  • 14.2 Key Players
  • 14.3 Profiles of Key Players
    • 14.3.1 Amazon Web Services (AWS)
      • 14.3.1.1 Company Overview
      • 14.3.1.2 Product Portfolio
    • 14.3.2 Google Inc.
      • 14.3.2.1 Company Overview
      • 14.3.2.2 Product Portfolio
      • 14.3.2.3 SWOT Analysis
    • 14.3.3 IBM
      • 14.3.3.1 Company Overview
      • 14.3.3.2 Product Portfolio
    • 14.3.4 Intel
      • 14.3.4.1 Company Overview
      • 14.3.4.2 Product Portfolio
      • 14.3.4.3 Financials
      • 14.3.4.4 SWOT Analysis
    • 14.3.5 Micron Technology
      • 14.3.5.1 Company Overview
      • 14.3.5.2 Product Portfolio
      • 14.3.5.3 Financials
      • 14.3.5.4 SWOT Analysis
    • 14.3.6 Microsoft Corporation
      • 14.3.6.1 Company Overview
      • 14.3.6.2 Product Portfolio
      • 14.3.6.3 Financials
      • 14.3.6.4 SWOT Analysis
    • 14.3.7 Nvidia
      • 14.3.7.1 Company Overview
      • 14.3.7.2 Product Portfolio
      • 14.3.7.3 Financials
      • 14.3.7.4 SWOT Analysis
    • 14.3.8 Qualcomm
      • 14.3.8.1 Company Overview
      • 14.3.8.2 Product Portfolio
      • 14.3.8.3 Financials
      • 14.3.8.4 SWOT Analysis
    • 14.3.9 Samsung Electronics
      • 14.3.9.1 Company Overview
      • 14.3.9.2 Product Portfolio
    • 14.3.10 Sensory Inc.
      • 14.3.10.1 Company Overview
      • 14.3.10.2 Product Portfolio
    • 14.3.11 Pathmind Inc.
      • 14.3.11.1 Company Overview
      • 14.3.11.2 Product Portfolio
    • 14.3.12 Xilinx
      • 14.3.12.1 Company Overview
      • 14.3.12.2 Product Portfolio
      • 14.3.12.3 Financials
      • 14.3.12.4 SWOT Analysis

List of Figures

  • Figure 1: Global: Deep Learning Market: Major Drivers and Challenges
  • Figure 2: Global: Deep Learning Market: Sales Value (in Billion US$), 2018-2023
  • Figure 3: Global: Deep Learning Market: Breakup by Product Type (in %), 2023
  • Figure 4: Global: Deep Learning Market: Breakup by Application (in %), 2023
  • Figure 5: Global: Deep Learning Market: Breakup by End-Use Industry (in %), 2023
  • Figure 6: Global: Deep Learning Market: Breakup by Architecture (in %), 2023
  • Figure 7: Global: Deep Learning Market: Breakup by Region (in %), 2023
  • Figure 8: Global: Deep Learning Market Forecast: Sales Value (in Billion US$), 2024-2032
  • Figure 9: Global: Deep Learning (Software) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 10: Global: Deep Learning (Software) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 11: Global: Deep Learning (Services) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 12: Global: Deep Learning (Services) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 13: Global: Deep Learning (Hardware) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 14: Global: Deep Learning (Hardware) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 15: Global: Deep Learning (Image Recognition) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 16: Global: Deep Learning (Image Recognition) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 17: Global: Deep Learning (Signal Recognition) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 18: Global: Deep Learning (Signal Recognition) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 19: Global: Deep Learning (Data Mining) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 20: Global: Deep Learning (Data Mining) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 21: Global: Deep Learning (Other Applications) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 22: Global: Deep Learning (Other Applications) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 23: Global: Deep Learning (Security) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 24: Global: Deep Learning (Security) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 25: Global: Deep Learning (Manufacturing) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 26: Global: Deep Learning (Manufacturing) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 27: Global: Deep Learning (Retail) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 28: Global: Deep Learning (Retail) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 29: Global: Deep Learning (Automotive) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 30: Global: Deep Learning (Automotive) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 31: Global: Deep Learning (Healthcare) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 32: Global: Deep Learning (Healthcare) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 33: Global: Deep Learning (Agriculture) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 34: Global: Deep Learning (Agriculture) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 35: Global: Deep Learning (Other End-Use Industries) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 36: Global: Deep Learning (Other End-Use Industries) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 37: Global: Deep Learning (RNN) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 38: Global: Deep Learning (RNN) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 39: Global: Deep Learning (CNN) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 40: Global: Deep Learning (CNN) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 41: Global: Deep Learning (DBN) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 42: Global: Deep Learning (DBN) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 43: Global: Deep Learning (DSN) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 44: Global: Deep Learning (DSN) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 45: Global: Deep Learning (GRU) Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 46: Global: Deep Learning (GRU) Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 47: North America: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 48: North America: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 49: United States: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 50: United States: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 51: Canada: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 52: Canada: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 53: Asia Pacific: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 54: Asia Pacific: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 55: China: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 56: China: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 57: Japan: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 58: Japan: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 59: India: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 60: India: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 61: South Korea: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 62: South Korea: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 63: Australia: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 64: Australia: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 65: Indonesia: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 66: Indonesia: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 67: Others: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 68: Others: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 69: Europe: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 70: Europe: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 71: Germany: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 72: Germany: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 73: France: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 74: France: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 75: United Kingdom: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 76: United Kingdom: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 77: Italy: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 78: Italy: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 79: Spain: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 80: Spain: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 81: Russia: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 82: Russia: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 83: Others: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 84: Others: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 85: Latin America: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 86: Latin America: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 87: Brazil: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 88: Brazil: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 89: Mexico: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 90: Mexico: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 91: Others: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 92: Others: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 93: Middle East and Africa: Deep Learning Market: Sales Value (in Million US$), 2018 & 2023
  • Figure 94: Middle East and Africa: Deep Learning Market Forecast: Sales Value (in Million US$), 2024-2032
  • Figure 95: Global: Deep Learning Industry: SWOT Analysis
  • Figure 96: Global: Deep Learning Industry: Value Chain Analysis
  • Figure 97: Global: Deep Learning Industry: Porter's Five Forces Analysis

List of Tables

  • Table 1: Global: Deep Learning Market: Key Industry Highlights, 2023 and 2032
  • Table 2: Global: Deep Learning Market Forecast: Breakup by Product Type (in Million US$), 2024-2032
  • Table 3: Global: Deep Learning Market Forecast: Breakup by Application (in Million US$), 2024-2032
  • Table 4: Global: Deep Learning Market Forecast: Breakup by End-Use Industry (in Million US$), 2024-2032
  • Table 5: Global: Deep Learning Market Forecast: Breakup by Architecture (in Million US$), 2024-2032
  • Table 6: Global: Deep Learning Market Forecast: Breakup by Region (in Million US$), 2024-2032
  • Table 7: Global: Deep Learning Market: Competitive Structure
  • Table 8: Global: Deep Learning Market: Key Players