市場調査レポート

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1792607

ディープラーニング市場：製品タイプ、用途、最終用途産業、アーキテクチャ、地域別規模、シェア、動向、予測：2025～2033年

Deep Learning Market Size, Share, Trends and Forecast by Product Type, Application, End-Use Industry, Architecture, and Region, 2025-2033

出版日: 2025年08月01日
発行: IMARC
ページ情報: 英文 135 Pages
納期: 2～3営業日

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概要
図表
目次

概要

サンプル依頼リストに入れる

世界のディープラーニング市場規模は2024年に309億米ドルに達しました。今後、IMARC Groupは、同市場が2033年までに4,234億米ドルに達し、2025～2033年にかけて29.92%の成長率（CAGR）を示すと予測しています。現在、北米が市場を独占しており、2024年には36.5%を超える大きな市場シェアを占めています。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推進する主要因となっています。

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、デジタル化の進展や、生データを自動抽出するディープラーニングの普及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してデータを処理するため、より効率的で正確な意思決定が可能になります。さらに、ヘルスケアにおけるサイバーセキュリティ、不正検知、医療画像分析、バーチャル患者支援などの幅広いサービス利用も、もう1つの大きな成長促進要因となっています。このほか、ビッグデータ分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。

米国は、人工知能（AI）技術の急速な発展とAI主導の研究開発への投資の増加により、主要な地域市場として際立っています。加えて、複雑なデータから実用的な洞察を得るための先進的データ分析の必要性も、特に金融、小売、ヘルスケアセグメントにおける成長の主要な促進要因となっています。また、AIイノベーションを奨励する政府の取り組みも、ディープラーニングが自律システムやスマートデバイスでますます使用されるようになっていることから、市場の成長をさらに後押ししています。2024年11月4日、Meta Platforms, Inc.は、米国政府機関や国家安全保障請負業者が同社の人工知能モデルを軍事用途に利用することを認めると宣言しました。同社によると、Llamaと呼ばれる同社のAIモデルを連邦政府機関が利用できるようにするといいます。同社は、Lockheed Martinやブーズ・アレンといった防衛関連企業や、パランティアやアンドゥリルといった防衛を専門とする技術企業と協力しています。このほか、盛んなeコマースやデジタルマーケティングセグメントでは、パーソナライズされた顧客体験や対象広告のためにディープラーニングが活用されています。さらに、最先端のAIソリューションを開発するためのハイテク大手と新興企業の提携も、米国のディープラーニング市場の堅調な成長に寄与しています。

ディープラーニング市場の動向

画像認識と音声認識におけるディープラーニング需要の高まり

画像内のパターンやオブジェクト、特徴を分析・識別する需要の高まりが、市場の成長を加速させています。さらに、ディープラーニング技術をベースとした医療用画像処理ソリューションは、異常検知や外科手術の支援機能など、医療セグメントでのアプリケーションとともに病気の診断サポートを提供するため、成長にプラスの影響を与えています。これに加えて、画像認識システムは、交通標識、歩行者、その他の障害物のリアルタイム検出を容易にし、自律走行車の検出において、交通の安全性と効率性の向上に役立ちます。さらに、NLPアプリケーションや音声アシスタントの作成に欠かせない音声認識もあります。また、ディープラーニングモデルは音声をテキストに書き起こすために採用され、Siri、Alexa、Google Assistant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、応答できるようにします。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）活動への投資の増加

ディープラーニングは急速なペースで進歩し続けており、さまざまな産業の組織がこの技術の機能と応用を向上させるために多額の投資を行っています。さらに、研究開発への投資は、性能、精度、効率を向上させる学習の側面や、新しいアルゴリズムやアーキテクチャの開発に対して行われ、それによって市場の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。スタンフォード大学のArtificial Indexによると、AIへの民間投資は2023年には全体的に減少したが、生成AIへの資金調達は劇的に増加し、2022年からほぼ8倍の252億米ドルに達しました。Hugging Face、Inflection、Anthropic、OpenAIといった著名な生成AI企業によって、多額の資金調達ラウンドが開示されました。さらに、ハードウェアの最適化も研究開発投資の焦点となっています。GPU（グラフィカルプロセッシングユニット）やTPU（テンソルプロセッシングユニット）など、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専用プロセッサの開発が進んでいます。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、企業にとってモデルがより身近でスケーラブルなものになります。

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、もう一つの大きな成長誘発要因となっています。世界の政府の取り組みが、ディープラーニングビジネスの拡大に拍車をかけています。例えば、欧州の連合のHorizon Europe Programは、ディープラーニングと人工知能の開発に向けて934億ユーロ（980億米ドル）を割り当てている（2021～2027年）。米国の国家AIイニシアティブ法は、AIの研究開発（R&D）、教育、標準開発のための資金を増やすために、5年間（2021～2026年）で約65億米ドルを提供しています。一方、インドの国家AI戦略は、ヘルスケア、教育、農業を優先し、2035年までにGDPを1兆米ドル押し上げると予想されています。これらの規制は、最先端のディープラーニングに対する国際的な投資を強調しています。

これとは別に、政府は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・ハブを設立する傾向にあり、これは研究者、学者、産業専門家のための共同スペースであり、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作り出しています。さらに、政府は官民パートナーシップに積極的に関与し、産業を超えた製品の採用を加速させ、責任あるAIの開発と展開を奨励する施策と施策を策定することで、市場の成長を促進しています。

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
- 市場内訳：国別

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

図表

List of Figures

Figure 1: Global: Deep Learning Market: Major Drivers and Challenges
Figure 2: Global: Deep Learning Market: Sales Value (in Billion USD), 2019-2024
Figure 3: Global: Deep Learning Market: Breakup by Product Type (in %), 2024
Figure 4: Global: Deep Learning Market: Breakup by Application (in %), 2024
Figure 5: Global: Deep Learning Market: Breakup by End-Use Industry (in %), 2024
Figure 6: Global: Deep Learning Market: Breakup by Architecture (in %), 2024
Figure 7: Global: Deep Learning Market: Breakup by Region (in %), 2024
Figure 8: Global: Deep Learning Market Forecast: Sales Value (in Billion USD), 2025-2033
Figure 9: Global: Deep Learning (Software) Market: Sales Value (in Million USD), 2019 & 2024
Figure 10: Global: Deep Learning (Software) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 11: Global: Deep Learning (Services) Market: Sales Value (in Million USD), 2019 & 2024
Figure 12: Global: Deep Learning (Services) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 13: Global: Deep Learning (Hardware) Market: Sales Value (in Million USD), 2019 & 2024
Figure 14: Global: Deep Learning (Hardware) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 15: Global: Deep Learning (Image Recognition) Market: Sales Value (in Million USD), 2019 & 2024
Figure 16: Global: Deep Learning (Image Recognition) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 17: Global: Deep Learning (Signal Recognition) Market: Sales Value (in Million USD), 2019 & 2024
Figure 18: Global: Deep Learning (Signal Recognition) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 19: Global: Deep Learning (Data Mining) Market: Sales Value (in Million USD), 2019 & 2024
Figure 20: Global: Deep Learning (Data Mining) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 21: Global: Deep Learning (Other Applications) Market: Sales Value (in Million USD), 2019 & 2024
Figure 22: Global: Deep Learning (Other Applications) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 23: Global: Deep Learning (Security) Market: Sales Value (in Million USD), 2019 & 2024
Figure 24: Global: Deep Learning (Security) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 25: Global: Deep Learning (Manufacturing) Market: Sales Value (in Million USD), 2019 & 2024
Figure 26: Global: Deep Learning (Manufacturing) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 27: Global: Deep Learning (Retail) Market: Sales Value (in Million USD), 2019 & 2024
Figure 28: Global: Deep Learning (Retail) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 29: Global: Deep Learning (Automotive) Market: Sales Value (in Million USD), 2019 & 2024
Figure 30: Global: Deep Learning (Automotive) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 31: Global: Deep Learning (Healthcare) Market: Sales Value (in Million USD), 2019 & 2024
Figure 32: Global: Deep Learning (Healthcare) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 33: Global: Deep Learning (Agriculture) Market: Sales Value (in Million USD), 2019 & 2024
Figure 34: Global: Deep Learning (Agriculture) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 35: Global: Deep Learning (Other End-Use Industries) Market: Sales Value (in Million USD), 2019 & 2024
Figure 36: Global: Deep Learning (Other End-Use Industries) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 37: Global: Deep Learning (RNN) Market: Sales Value (in Million USD), 2019 & 2024
Figure 38: Global: Deep Learning (RNN) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 39: Global: Deep Learning (CNN) Market: Sales Value (in Million USD), 2019 & 2024
Figure 40: Global: Deep Learning (CNN) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 41: Global: Deep Learning (DBN) Market: Sales Value (in Million USD), 2019 & 2024
Figure 42: Global: Deep Learning (DBN) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 43: Global: Deep Learning (DSN) Market: Sales Value (in Million USD), 2019 & 2024
Figure 44: Global: Deep Learning (DSN) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 45: Global: Deep Learning (GRU) Market: Sales Value (in Million USD), 2019 & 2024
Figure 46: Global: Deep Learning (GRU) Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 47: North America: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 48: North America: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 49: United States: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 50: United States: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 51: Canada: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 52: Canada: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 53: Asia Pacific: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 54: Asia Pacific: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 55: China: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 56: China: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 57: Japan: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 58: Japan: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 59: India: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 60: India: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 61: South Korea: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 62: South Korea: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 63: Australia: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 64: Australia: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 65: Indonesia: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 66: Indonesia: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 67: Others: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 68: Others: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 69: Europe: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 70: Europe: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 71: Germany: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 72: Germany: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 73: France: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 74: France: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 75: United Kingdom: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 76: United Kingdom: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 77: Italy: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 78: Italy: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 79: Spain: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 80: Spain: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 81: Russia: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 82: Russia: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 83: Others: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 84: Others: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 85: Latin America: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 86: Latin America: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 87: Brazil: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 88: Brazil: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 89: Mexico: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 90: Mexico: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 91: Others: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 92: Others: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
Figure 93: Middle East and Africa: Deep Learning Market: Sales Value (in Million USD), 2019 & 2024
Figure 94: Middle East and Africa: Deep Learning Market Forecast: Sales Value (in Million USD), 2025-2033
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, 2024 and 2033
Table 2: Global: Deep Learning Market Forecast: Breakup by Product Type (in Million USD), 2025-2033
Table 3: Global: Deep Learning Market Forecast: Breakup by Application (in Million USD), 2025-2033
Table 4: Global: Deep Learning Market Forecast: Breakup by End-Use Industry (in Million USD), 2025-2033
Table 5: Global: Deep Learning Market Forecast: Breakup by Architecture (in Million USD), 2025-2033
Table 6: Global: Deep Learning Market Forecast: Breakup by Region (in Million USD), 2025-2033
Table 7: Global: Deep Learning Market: Competitive Structure
Table 8: Global: Deep Learning Market: Key Players

Product Code: SR112025A1941

The global deep learning market size reached USD 30.9 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 423.4 Billion by 2033, exhibiting a growth rate (CAGR) of 29.92% during 2025-2033. North America currently dominates the market, holding a significant market share of over 36.5% in 2024. 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.

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, are influencing the 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 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.

The United States stands out as a key regional market, driven by rapid advancements in artificial intelligence (AI) technologies and increasing investments in AI-driven research and development. In addition, the need for sophisticated data analytics to yield actionable insights from complex data is another major driver of growth, especially in the finance, retail, and healthcare sectors. Government efforts to encourage AI innovation are also driving the market growth further, as deep learning is increasingly being used in autonomous systems and smart devices. On 4th November 2024, Meta Platforms, Inc. declared that it will allow U.S. government agencies and national security contractors to utilize its artificial intelligence models for military applications. The firm said it will make its AI models, which are called Llama, available to federal agencies. It is working with defense contractors such as Lockheed Martin and Booz Allen, as well as technology companies specializing in defense, such as Palantir and Anduril. Besides this, the flourishing e-commerce and digital marketing sectors are leveraging deep learning for personalized customer experiences and targeted advertising. Additionally, partnerships between tech giants and startups to develop cutting-edge AI solutions contribute to the robust growth of the deep learning market in the United States.

Deep Learning Market Trends:

The rising demand for deep learning for image and speech recognition

The growing demand to analyse and identify patterns, objects, and features within images is escalating the market growth. Moreover, deep learning technology-based medical imaging solutions provide diagnostic support for diseases along with anomaly detection and supportive features in surgical procedures and other applications in the health department, thus impacting the growth positively. In addition to this, image recognition systems facilitate real-time detection of traffic signs, pedestrians, and other obstacles in the detection of autonomous vehicles that help increase road safety and efficiency of the same. In addition, there is speech recognition, which proves crucial in the making of NLP applications and a voice assistant. 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 investments in research and development (R&D) activities

Deep learning continues to advance at a rapid pace, and organizations in different industries are investing heavily in order to improve the capabilities and applications of this technology. Furthermore, investments in R&D are made on aspects of learning and the development of new algorithms and architectures that enhance performance, accuracy, and efficiency, thereby affecting 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. According to the Artificial Index by Stanford University, private investment in AI fell overall in 2023, but financing for generative AI increased dramatically, almost octupling from 2022 to USD 25.2 Billion. Significant fundraising rounds were disclosed by prominent generative AI companies, such as Hugging Face, Inflection, Anthropic, and OpenAI. 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. Global government initiatives are fuelling the expansion of the deep learning business. For instance, the Horizon Europe Program of the European Union allots €93.4 Billion (USD 98 Billion) (2021-2027) towards developments in deep learning and artificial intelligence. The U.S. National AI Initiative Act provides nearly USD 6.5 Billion over the five years (2021-2026) to increase funding for AI research and development (R&D), education, and standards development. In the meantime, India's National AI Strategy, which prioritises healthcare, education, and agriculture, is anticipated to boost GDP by USD 1 Trillion by 2035. These regulations highlight international investments in cutting-edge deep learning.

Apart from this, governments tend to create AI-focused centers of excellence and innovation hubs which are collaborative spaces for researchers, academics, and industry experts that facilitate knowledge sharing, networking, and interdisciplinary research, creating an environment that is conducive to breakthrough discoveries in deep learning. In addition, governments actively engage in public-private partnerships to accelerate the adoption of products across industries and create policies and regulations that encourage responsible AI development and deployment thus propelling the market growth.

Deep Learning Industry Segmentation:

Analysis by Product Type:

Software
Services
Hardware

Software leads the market with around 48.2% of market share in 2024. Software is crucial in the development and implementation of deep learning algorithms and models. It is a source that offers all the necessary tools and frameworks for researchers, data scientists, and developers to make complex neural networks and train them efficiently. Hence, software solutions have become the key to unlock the power of technology. Additionally, flexibility and scalability offered by the software make it highly attractive to businesses of all 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.

Analysis by Application:

Image Recognition
Signal Recognition
Data Mining
Others

Image recognition leads the market with around 40.5%of market share in 2024. Image recognition is currently dominating the market 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.

Analysis by End Use Industry:

Security
Manufacturing
Retail
Automotive
Healthcare
Agriculture
Others

Security leads the market with around 12.8% of market share in 2024. Deep learning technology provides unparalleled capabilities in the detection, analysis, and response to sophisticated security breaches and attacks. Additionally, the growing demand for more powerful and sophisticated solutions to deal with the changing nature of cyber threats, is driving the market growth. In the cybersecurity domain, deep learning algorithms have an advantage over traditional security systems as they are efficient in detecting anomalies, patterns, and suspicious activities.

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.

Analysis by Architecture:

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.

In addition, CNNs are used for image and video processing tasks because they have the ability to extract features well using convolutional layers, which scan input data with small filters to identify patterns and spatial relationships. CNNs are widely used in image recognition, object detection, and image classification tasks because they can automatically learn relevant visual features. Apart from this, DBN stands for deep belief networks. These are generative models, consisting of multiple layers of stochastic, latent variables. They are used in unsupervised learning tasks, such as feature learning and dimensionality reduction. Hence, they find their use 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.

Regional Analysis:

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

In 2024, North America accounted for the largest market share of over 36.5%. 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.

Key Regional Takeaways:

United States Deep Learning Market Analysis

In 2024, US accounted for around 70.00% of the total North America deep learning market. Due to extensive use of machine learning applications, substantial investments in artificial intelligence (AI) research, and improvements in processing power, the US leads the world in the deep learning market. The US is a major leader in this technology. U.S.-based institutes produced 61 noteworthy AI models in 2023, significantly more than the European Union's 21 and China's 15. Innovation in this field is being led by companies such as Google, Microsoft, and NVIDIA, especially in areas like autonomous systems, computer vision, and natural language processing (NLP).

One of the main forces behind the advancements in drug development, personalised medicine, and diagnostics is the use of deep learning in healthcare. For instance, medical photographs may now be analysed with precision levels of 90% by incorporating deep learning algorithms. Deep learning is also being quickly incorporated into industries including finance, retail, and automotive for customer insights and predictive analytics. Big data's growth has also increased demand; according to current figures, IBM estimates that 2.5 quintillion bytes of data are created daily, which is so enormous that 90% of the world's data was created in the last two years. Accessibility is being further improved and market growth is being propelled by cloud-based platforms and the rise of AI-as-a-Service offerings by major providers.

Europe Deep Learning Market Analysis

The market for deep learning in Europe is growing because of its rich research infrastructure, strong government efforts, and growing industry use. To encourage the use of AI and deep learning, the European Union's Digital Europe Programme has set aside €7.5 Billion (Approximately USD 7.9 Billion) for 2021-2027, with a focus on applications in smart manufacturing, driverless cars, and healthcare. Additionally, The European Union plans to invest 1.4 Billion Euros (USD 1.5 Billion) to help the deep tech research industry in the region in the year 2025. The European Innovation Council (EIC), a division of the EU's research and innovation program, will provide the financing, which is an investment increase of around 200 million euros over 2024. Leading nations including the UK, France, and Germany are utilising deep learning for sophisticated robotics and industrial automation in accordance with Industry 4.0 objectives.

Major end use industries for this technology include the automotive and healthcare industries. In radiology and pathology, deep learning algorithms are frequently employed to increase diagnostic accuracy. Deep learning is being incorporated into self-driving technology in the automobile sector, with manufacturers such as Daimler and BMW making significant investments in AI-powered solutions. Furthermore, the use of deep learning to smart grids and renewable energy management has been accelerated by Europe's emphasis on sustainability. While Europe's strict data protection regulations, such as GDPR, have prompted the development of safe and moral AI frameworks, the expanding 5G infrastructure is also facilitating the adoption of edge AI solutions.

Asia Pacific Deep Learning Market Analysis

The deep learning market in Asia-Pacific is expanding at the quickest rate due to factors like growing investments in AI, rapid digitisation, and an increasingly tech-savvy populace. The top donors are India, South Korea, Japan, and China. The adoption of AI and generative AI technologies, such as software, services, and hardware made for AI-driven systems, is accelerating dramatically across the Asia/Pacific region. AI and Generative AI (GenAI) investments in the region are expected to reach USD 110 Billion by 2028, rising at a compound annual growth rate (CAGR) of 24.0% from 2023 to 2028, according to the most recent Worldwide AI and Generative AI Spending Guide published by International Development Corporation. The software and information services sector is one of the top adopters of AI, with a market share of 23.8% in 2024.

China's AI 2030 plan, which includes large investments in deep learning research, aims to establish the nation as a global leader in AI. With businesses like Toyota and Hyundai integrating AI in manufacturing and mobility solutions, South Korea and Japan are utilising deep learning in robots and autonomous vehicles. The proliferation of digital transactions and consumer data in India is propelling the use of deep learning in finance and e-commerce. Deep learning is also being used by the region's gaming and entertainment sectors to create immersive experiences and real-time personalisation.

Latin America Deep Learning Market Analysis

The growing adoption of AI and digital transformation across multiple industries is propelling the deep learning industry in Latin America. In the region, Brazil and Mexico are at the forefront in both application and investment. Deep learning is being applied in Brazil's vast agribusiness sector to improve productivity through crop monitoring and predictive analytics. Deep learning is being used in Mexico's retail and e-commerce sectors to forecast demand and gain insights into customers. Deep learning is also being used by the Latin American financial services industry for credit risk assessment and fraud detection, as fintech firms embrace AI-powered systems. Deep learning is also for identifying pavement failures in Latin American and the Caribbean. For instance, The Inter-American Development Bank (IDB) created the Pavimenta2 platform to evaluate road signage and to detect, monitor, and quantify pavement defects. Pavimenta2 uses computer vision technology, artificial intelligence (AI), and deep learning to automatically measure the locations and quantities of blurred lines, linear cracking, transversal cracking, crocodile cracking, rutting, and other failures by simply driving through the roadway network with a mounted cell phone or GoPro. The recorded video is then uploaded.

Middle East and Africa Deep Learning Market Analysis

The deep learning market in the Middle East and Africa (MEA) is in its initial stage but is witnessing rapid growth due to increasing investments in AI and smart city initiatives. With an emphasis on AI and deep learning technologies in Saudi Vision 2030 and Dubai's Smart City Strategy, nations like the United Arab Emirates and Saudi Arabia are leading the way in this adoption. Deep learning applications are also being used by the region's retail and healthcare industries to improve diagnostic precision and provide individualised services. For instance, AI-driven algorithms are being used by telemedicine companies in the United Arab Emirates to facilitate remote medical services. Additionally, the introduction of 5G networks and improvements in cloud infrastructure are enabling deep learning solutions to gain traction. The market is expected to pick up in the coming years. According to a survey conducted by Microsoft among AI leaders in 112 companies, across 7 sectors and 5 countries in the Middle East and Africa, it was found out that 89% of the respondents expect AI to generate business benefits by optimizing their companies' operations in the future.

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 provides a comprehensive analysis of the competitive landscape in the keyword market with detailed profiles of all major companies, including:

Amazon Web Services (AWS)
Google Inc.
IBM
Intel
Micron Technology
Microsoft Corporation
Nvidia
Qualcomm
Samsung Electronics
Sensory Inc.,
Pathmind, Inc.
Xilinx

Key Questions Answered in This Report

1.What is deep learning?
2.How big is the global deep learning market?
3.What is the expected growth rate of the global deep learning market during 2025-2033?
4.What are the key factors driving the global deep learning market?
5.What is the leading segment of the global deep learning market based on product type?
6.What is the leading segment of the global deep learning market based on application?
7.What is the leading segment of the global deep learning market based on 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 keyword 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 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

ディープラーニング市場：製品タイプ、用途、最終用途産業、アーキテクチャ、地域別規模、シェア、動向、予測：2025～2033年

Deep Learning Market Size, Share, Trends and Forecast by Product Type, Application, End-Use Industry, Architecture, and Region, 2025-2033

ディープラーニング市場の動向

目次

第1章 序文

第2章 調査範囲と調査手法

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

第5章 世界のディープラーニング市場

第6章 市場内訳：製品タイプ別

第7章 市場内訳：用途別

第8章 市場内訳：最終用途産業別

第9章 市場内訳：アーキテクチャ

第10章 市場内訳：地域別

第11章 SWOT分析

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

第14章 競合情勢