ディープラーニング市場レポート：製品タイプ、用途、最終用途産業、アーキテクチャ、地域別、2024～2032年

世界のディープラーニング市場規模は2023年に235億米ドルに達しました。今後、IMARC Groupは、2024～2032年の間に31.5%の成長率（CAGR）を示し、2032年までに2,951億米ドルに達すると予測しています。人工知能（AI）導入の増加、データ処理の進歩、画像認識や音声認識の需要拡大、研究開発（R&D）への投資、ビッグデータやクラウドコンピューティング技術の導入などが市場を後押しする主要要因となっています。

ディープラーニングは人工知能（AI）の一セグメントであり、膨大なデータから学習し意思決定を行うために人工ニューラルネットワークを学習させる。これらのニューラルネットワークは、相互に接続されたノードの層で構成され、人間の脳の構造を模倣しています。ネットワークは内部パラメーターを反復的に調整し、データ内のパターン、特徴、表現を特定することで、物体の認識、音声の理解、言語の翻訳、さらには戦略的なゲームを可能にします。また、コンピュータ・ビジョン、自然言語処理（NLP）、ロボット工学など様々な領域を変革し、従来の機械学習アプローチでは困難とされていた課題において目覚ましいブレークスルーを達成しています。

市場は主に、情報技術（IT）産業の大幅な拡大によって牽引されています。また、デジタル化の動向の高まりや、生データを自動的に抽出するディープラーニングの普及により、複雑な現実世界の問題を高い精度と効率で解決する強力なツールとなっていることが、市場成長に影響を与えています。また、利用可能なデータを自動的に分析することでデータを処理し、より効率的で正確な意思決定をもたらします。さらに、サイバーセキュリティ、不正検知、医療画像分析、医療におけるバーチャル患者支援など、幅広いサービス利用も大きな成長促進要因となっています。このほか、ビッグデータ解析とクラウドコンピューティングの統合や、ハードウェアとソフトウェアの処理を改善するための継続的な研究開発（R&D）が、市場の成長をさらに加速させています。さらに、これらの技術が提供するスケーラビリティと計算能力により、企業は膨大なデータセットを効率的に処理・分析できるため、市場の展望は明るいです。

ディープラーニング市場の動向と促進要因：

画像認識と音声認識に対するディープラーニングの需要の高まり

画像内のパターン、物体、特徴を分析・識別する需要の高まりが、市場の成長をエスカレートさせています。さらに、ディープラーニングを搭載した医療用画像処理システムは、医療のセグメントにおいて、病気の診断、異常の検出、手術計画の補助を支援し、市場成長に影響を与えています。さらに、自律走行車では、画像認識によって交通標識、歩行者、障害物をリアルタイムで識別できるため、自動運転車の安全性と効率が向上し、これも大きな成長促進要因となっています。このほか、音声認識は自然言語処理（NLP）アプリケーションや音声アシスタントの開発に不可欠です。また、音声をテキストに書き起こすためにディープラーニングモデルが採用され、Siri、Alexa、Googleアシスタントなどの音声制御バーチャルアシスタントがユーザーのコマンドを正確に理解し応答できるようになっています。これにより、人々の技術との関わり方が一変し、ハンズフリーで直感的なユーザー体験が可能になりました。さらに、カスタマーサービスセンター、コールセンター、言語翻訳サービスにおける音声認識製品の採用は、コミュニケーションを合理化し、応答時間を改善しているため、市場の成長を促進しています。

研究開発（R&D）への投資の増加

ディープラーニングは急速に進化し続けており、各業界の企業はこの最先端技術の能力と応用を強化するために多額のリソースを割いています。さらに、研究開発への投資は、学習のさまざまな側面や、パフォーマンス、精度、効率を向上させる新規アルゴリズムやアーキテクチャの開発に重点を置いており、市場成長に影響を与えています。また、研究者は、自然言語処理、コンピュータ・ビジョン、その他のAI主導のタスクにおけるブレークスルーを達成するために、注意メカニズム、トランスフォーマー、生成的敵対ネットワーク（GAN）などの革新的な技術を継続的に探求しています。さらに、ハードウェアの最適化も研究開発投資の焦点のひとつです。各組織は、ディープラーニングの計算を高速化するために設計されたグラフィカル・プロセッシング・ユニット（GPU）やテンソル・プロセッシング・ユニット（TPU）などの専用プロセッサを開発しています。このようなハードウェアの進歩により、学習時間や推論の高速化が可能になり、企業にとってより利用しやすくスケーラブルなモデルとなります。

有利な政府イニシアティブの実施

政府の支援とイニシアチブは、市場の成長を促進する上で不可欠です。さらに、政府は人工知能（AI）の変革の可能性を認識し、AIの研究開発プロジェクトに積極的に投資し、研究開発を促進しているため、市場の成長に影響を与えています。さらに、政府機関からの財政投資により、大学、研究機関、非公開会社は、技術革新の限界を押し広げ、技術的進歩を促進する野心的な深層学習プロジェクトを実施することができ、これも大きな成長誘発要因となっています。これに加え、政府はしばしばAIに特化したセンター・オブ・エクセレンスやイノベーション・ハブを設立し、研究者、学者、業界専門家のための共同スペースとして機能させ、知識の共有、ネットワーキング、学際的研究を促進し、ディープラーニングにおける画期的な発見を助長する環境を育成しています。さらに、政府は官民パートナーシップに積極的に関与し、業界全体への製品採用を加速させ、責任あるAIの開発と展開を奨励する政策と政策を策定することで、市場の成長を促進しています。

目次

第1章 序文

第2章 調査範囲と調査手法

• 調査目的
• 利害関係者
• データソース
  • 一次情報
  • 二次情報
• 市場推定
  • ボトムアップアプローチ
  • トップダウンアプローチ
• 調査手法

第3章 エグゼクティブサマリー

第4章 イントロダクション

• 概要
• 主要産業動向

第5章 世界のディープラーニング市場

• 市場概要
• 市場実績
• COVID-19の影響
• 市場予測

第6章 市場内訳：製品タイプ別

• ソフトウェア
• サービス
• ハードウェア

第7章 市場内訳：用途別

• 画像認識
• 信号認識
• データ鉱業
• その他

第8章 市場内訳：最終用途産業別

• セキュリティ
• 製造業
• 小売
• 自動車
• 医療
• 農業
• その他

第9章 市場内訳：アーキテクチャ別

• RNN
• CNN
• DBN
• DSN
• GRU

第10章 市場内訳：地域別

• 北米
  • 米国
  • カナダ
• アジア太平洋
  • 中国
  • 日本
  • インド
  • 韓国
  • オーストラリア
  • インドネシア
  • その他
• 欧州
  • ドイツ
  • フランス
  • 英国
  • イタリア
  • スペイン
  • ロシア
  • その他
• ラテンアメリカ
  • ブラジル
  • メキシコ
  • その他
• 中東・アフリカ
  • 市場動向
  • 市場内訳：国別
  • 市場予測

第11章 SWOT分析

• 概要
• 強み
• 弱み
• 機会
• 脅威

第12章 バリューチェーン分析

第13章 ポーターのファイブフォース分析

• 概要
• 買い手の交渉力
• 供給企業の交渉力
• 競合の程度
• 新規参入業者の脅威
• 代替品の脅威

第14章 競合情勢

• 市場構造
• 主要企業
• 主要企業のプロファイル
  • Amazon Web Services（AWS）
  • Google Inc.
  • IBM
  • Intel
  • Micron Technology
  • Microsoft Corporation
  • Nvidia
  • Qualcomm
  • Samsung Electronics
  • Sensory Inc.
  • Pathmind Inc.
  • Xilinx

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

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