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商品編碼

1746853

日本深度學習市場報告（按產品類型、應用、最終用途產業、架構和地區）2025-2033

Japan Deep Learning Market Report by Product Type, Application, End Use Industry, Architecture, and Region 2025-2033

出版日期: 2025年06月02日 | 出版商:

IMARC | 英文 118 Pages | 商品交期: 5-7個工作天內

價格

簡介目錄

2024年，日本深度學習市場規模達18.275億美元。展望未來， IMARC Group預計到2033年，市場規模將達到299.86億美元，2025-2033年期間的複合年成長率（CAGR）為36.5%。來自社交媒體、物聯網設備和感測器等各種來源的數位資料日益增多，為深度學習演算法提供了豐富的資訊來源，推動著市場的發展。

深度學習是人工智慧的一個子集，它模仿人腦的神經網路來解決複雜的任務。它涉及訓練深度神經網路（由許多相互連接的人工神經元層組成），以從資料中學習模式和表示。這些網路擅長圖像和語音辨識、自然語言處理甚至自主決策等任務。深度學習的強大之處在於它能夠自動從原始資料中發現和提取特徵，從而無需手動進行特徵工程。它依靠大型資料集和強大的運算硬體（尤其是 GPU）來有效地訓練模型。流行的深度學習架構包括用於影像分析的捲積神經網路 (CNN) 和用於序列資料的循環神經網路 (RNN)。深度學習的應用非常廣泛，包括自動駕駛汽車、醫療診斷、推薦系統等。它的不斷發展和創新使其成為一項變革性技術，透過使機器能夠像人類一樣學習和決策，它有可能徹底改變各個行業。

日本深度學習市場趨勢：

日本深度學習市場的發展受到一系列因素的共同推動，這些因素改變了人工智慧 (AI) 的模式。首先，資料可用性的指數級成長，加上巨量資料分析的興起，為深度學習演算法的蓬勃發展鋪平了道路。此外，在 GPU 技術和雲端運算創新的推動下，運算能力的不斷提升使得以前所未有的規模和速度訓練深度神經網路成為可能。此外，醫療保健、金融和自動駕駛汽車等行業對深度學習的日益普及，也導致對深度學習解決方案的需求激增。這種蓬勃發展的需求不僅源於改進決策和自動化的潛力，也源於從海量資料集中提取有意義洞察的需求日益成長。總而言之，日本深度學習市場預計將受到資料豐富、運算能力強大、應用領域不斷擴展以及工具便利性等因素的共同驅動，為該領域的持續成長和創新奠定基礎。

日本深度學習市場區隔：

產品類型洞察：

軟體
服務
硬體

應用程式洞察：

影像辨識
訊號識別
資料探勘
其他

最終用途行業洞察：

安全
製造業
零售
汽車
衛生保健
農業
其他

架構見解：

循環神經網路
CNN
資料庫
資料安全網路
格魯烏

競爭格局：

市場研究報告也對競爭格局進行了全面的分析。報告涵蓋了市場結構、關鍵參與者定位、最佳制勝策略、競爭儀錶板和公司評估象限等競爭分析。此外，報告還提供了所有主要公司的詳細資料。

本報告回答的關鍵問題：

日本深度學習市場目前表現如何？未來幾年會如何表現？
新冠疫情對日本深度學習市場有何影響？
日本深度學習市場依產品類型分類是怎樣的？
日本深度學習市場按應用分類是怎樣的？
日本深度學習市場依最終用途產業分類的狀況如何？
日本深度學習市場在架構上是如何分割的？
日本深度學習市場的價值鏈分為哪些階段？
日本深度學習的關鍵促進因素和挑戰是什麼？
日本深度學習市場的結構是怎麼樣的？主要參與者有哪些？
日本深度學習市場的競爭程度如何？

本報告回答的關鍵問題：

日本深度學習市場目前表現如何？未來幾年會如何表現？
新冠疫情對日本深度學習市場有何影響？
日本深度學習市場依產品類型分類是怎樣的？
日本深度學習市場按應用分類是怎樣的？
日本深度學習市場依最終用途產業分類的狀況如何？
日本深度學習市場在架構上是如何分割的？
日本深度學習市場的價值鏈分為哪些階段？
日本深度學習的關鍵促進因素和挑戰是什麼？
日本深度學習市場的結構是怎麼樣的？主要參與者有哪些？
日本深度學習市場的競爭程度如何？

Japan deep learning market size reached USD 1,827.5 Million in 2024. Looking forward, IMARC Group expects the market to reach USD 29,986.0 Million by 2033, exhibiting a growth rate (CAGR) of 36.5% during 2025-2033. The increasing proliferation of digital data from various sources, including social media, IoT devices, and sensors, that provides a rich source of information for deep learning algorithms, is driving the market.

Deep learning is a subset of artificial intelligence that mimics the human brain's neural networks to solve complex tasks. It involves training deep neural networks, which are composed of many interconnected layers of artificial neurons, to learn patterns and representations from data. These networks excel at tasks like image and speech recognition, natural language processing, and even autonomous decision-making. Deep learning's power lies in its ability to automatically discover and extract features from raw data, eliminating the need for manual feature engineering. It relies on large datasets and powerful computing hardware, particularly GPUs, to train models effectively. Popular deep learning architectures include convolutional neural networks (CNNs) for image analysis and recurrent neural networks (RNNs) for sequential data. The applications of deep learning are vast and include self-driving cars, medical diagnosis, recommendation systems, and more. Its continuous development and innovation have made it a transformative technology with the potential to revolutionize various industries by enabling machines to learn and make decisions like humans.

Japan Deep Learning Market Trends:

The deep learning market in Japan is propelled by a confluence of factors that have transformed the landscape of artificial intelligence (AI). Firstly, the exponential growth of data availability, coupled with the rise of big data analytics, has paved the way for deep learning algorithms to thrive. Moreover, the continuous advancement in computing power, driven by innovations in GPU technology and cloud computing, has made it feasible to train deep neural networks at an unprecedented scale and speed. Furthermore, the increased adoption of deep learning across industries such as healthcare, finance, and autonomous vehicles has led to a surge in demand for deep learning solutions. This burgeoning demand is not only fueled by the promise of improved decision-making and automation but also by the escalating need to extract meaningful insights from vast datasets. In sum, the deep learning market in Japan is expected to be driven by a synergy of data abundance, computational prowess, expanding application domains, and accessible tools, setting the stage for continued growth and innovation in the field.

Japan Deep Learning Market Segmentation:

Product Type Insights:

Software
Services
Hardware

Application Insights:

Image Recognition
Signal Recognition
Data Mining
Others

End Use Industry Insights:

Security
Manufacturing
Retail
Automotive
Healthcare
Agriculture
Others

Architecture Insights:

Competitive Landscape:

The market research report has also provided a comprehensive analysis of the competitive landscape. Competitive analysis such as market structure, key player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles of all major companies have been provided.

Key Questions Answered in This Report:

How has the Japan deep learning market performed so far and how will it perform in the coming years?
What has been the impact of COVID-19 on the Japan deep learning market?
What is the breakup of the Japan deep learning market on the basis of product type?
What is the breakup of the Japan deep learning market on the basis of application?
What is the breakup of the Japan deep learning market on the basis of end use industry?
What is the breakup of the Japan deep learning market on the basis of architecture?
What are the various stages in the value chain of the Japan deep learning market?
What are the key driving factors and challenges in the Japan deep learning?
What is the structure of the Japan deep learning market and who are the key players?
What is the degree of competition in the Japan deep learning market?

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 Japan Deep Learning Market - Introduction

4.1 Overview
4.2 Market Dynamics
4.3 Industry Trends
4.4 Competitive Intelligence

5 Japan Deep Learning Market Landscape

5.1 Historical and Current Market Trends (2019-2024)
5.2 Market Forecast (2025-2033)

6 Japan Deep Learning Market - Breakup by Product Type

6.1 Software
- 6.1.1 Overview
- 6.1.2 Historical and Current Market Trends (2019-2024)
- 6.1.3 Market Forecast (2025-2033)
6.2 Services
- 6.2.1 Overview
- 6.2.2 Historical and Current Market Trends (2019-2024)
- 6.2.3 Market Forecast (2025-2033)
6.3 Hardware
- 6.3.1 Overview
- 6.3.2 Historical and Current Market Trends (2019-2024)
- 6.3.3 Market Forecast (2025-2033)

7 Japan Deep Learning Market - Breakup by Application

7.1 Image Recognition
- 7.1.1 Overview
- 7.1.2 Historical and Current Market Trends (2019-2024)
- 7.1.3 Market Forecast (2025-2033)
7.2 Signal Recognition
- 7.2.1 Overview
- 7.2.2 Historical and Current Market Trends (2019-2024)
- 7.2.3 Market Forecast (2025-2033)
7.3 Data Mining
- 7.3.1 Overview
- 7.3.2 Historical and Current Market Trends (2019-2024)
- 7.3.3 Market Forecast (2025-2033)
7.4 Others
- 7.4.1 Historical and Current Market Trends (2019-2024)
- 7.4.2 Market Forecast (2025-2033)

8 Japan Deep Learning Market - Breakup by End Use Industry

8.1 Security
- 8.1.1 Overview
- 8.1.2 Historical and Current Market Trends (2019-2024)
- 8.1.3 Market Forecast (2025-2033)
8.2 Manufacturing
- 8.2.1 Overview
- 8.2.2 Historical and Current Market Trends (2019-2024)
- 8.2.3 Market Forecast (2025-2033)
8.3 Retail
- 8.3.1 Overview
- 8.3.2 Historical and Current Market Trends (2019-2024)
- 8.3.3 Market Forecast (2025-2033)
8.4 Automotive
- 8.4.1 Overview
- 8.4.2 Historical and Current Market Trends (2019-2024)
- 8.4.3 Market Forecast (2025-2033)
8.5 Healthcare
- 8.5.1 Overview
- 8.5.2 Historical and Current Market Trends (2019-2024)
- 8.5.3 Market Forecast (2025-2033)
8.6 Agriculture
- 8.6.1 Overview
- 8.6.2 Historical and Current Market Trends (2019-2024)
- 8.6.3 Market Forecast (2025-2033)
8.7 Others
- 8.7.1 Historical and Current Market Trends (2019-2024)
- 8.7.2 Market Forecast (2025-2033)

9 Japan Deep Learning Market - Breakup by Architecture

9.1 RNN
- 9.1.1 Overview
- 9.1.2 Historical and Current Market Trends (2019-2024)
- 9.1.3 Market Forecast (2025-2033)
9.2 CNN
- 9.2.1 Overview
- 9.2.2 Historical and Current Market Trends (2019-2024)
- 9.2.3 Market Forecast (2025-2033)
9.3 DBN
- 9.3.1 Overview
- 9.3.2 Historical and Current Market Trends (2019-2024)
- 9.3.3 Market Forecast (2025-2033)
9.4 DSN
- 9.4.1 Overview
- 9.4.2 Historical and Current Market Trends (2019-2024)
- 9.4.3 Market Forecast (2025-2033)
9.5 GRU
- 9.5.1 Overview
- 9.5.2 Historical and Current Market Trends (2019-2024)
- 9.5.3 Market Forecast (2025-2033)

10 Japan Deep Learning Market - Competitive Landscape

10.1 Overview
10.2 Market Structure
10.3 Market Player Positioning
10.4 Top Winning Strategies
10.5 Competitive Dashboard
10.6 Company Evaluation Quadrant

11 Profiles of Key Players

11.1 Company A
- 11.1.1 Business Overview
- 11.1.2 Services Offered
- 11.1.3 Business Strategies
- 11.1.4 SWOT Analysis
- 11.1.5 Major News and Events
11.2 Company B
- 11.2.1 Business Overview
- 11.2.2 Services Offered
- 11.2.3 Business Strategies
- 11.2.4 SWOT Analysis
- 11.2.5 Major News and Events
11.3 Company C
- 11.3.1 Business Overview
- 11.3.2 Services Offered
- 11.3.3 Business Strategies
- 11.3.4 SWOT Analysis
- 11.3.5 Major News and Events
11.4 Company D
- 11.4.1 Business Overview
- 11.4.2 Services Offered
- 11.4.3 Business Strategies
- 11.4.4 SWOT Analysis
- 11.4.5 Major News and Events
11.5 Company E
- 11.5.1 Business Overview
- 11.5.2 Services Offered
- 11.5.3 Business Strategies
- 11.5.4 SWOT Analysis
- 11.5.5 Major News and Events

12 Japan Deep Learning Market - Industry Analysis

12.1 Drivers, Restraints, and Opportunities
- 12.1.1 Overview
- 12.1.2 Drivers
- 12.1.3 Restraints
- 12.1.4 Opportunities
12.2 Porters Five Forces Analysis
- 12.2.1 Overview
- 12.2.2 Bargaining Power of Buyers
- 12.2.3 Bargaining Power of Suppliers
- 12.2.4 Degree of Competition
- 12.2.5 Threat of New Entrants
- 12.2.6 Threat of Substitutes
12.3 Value Chain Analysis