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Deep Learning Market - Cumulative Impact of United States Tariffs 2025 - Global Forecast to 2030- Product Image
Deep Learning Market - Cumulative Impact of United States Tariffs 2025 - Global Forecast to 2030- Product Image
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Deep Learning Market - Cumulative Impact of United States Tariffs 2025 - Global Forecast to 2030

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    Report

  • 195 Pages
  • May 2025
  • Region: Global, United States
  • 360iResearch™
  • ID: 6083967
1h Free Analyst Time
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The Deep Learning Market grew from USD 7.24 billion in 2024 to USD 9.49 billion in 2025. It is expected to continue growing at a CAGR of 30.52%, reaching USD 35.82 billion by 2030.

In an era driven by exponential data growth and unprecedented computational power, deep learning has emerged as a cornerstone of technological advancement. As organizations across industries seek to harness complex datasets for strategic advantage, deep learning offers a suite of algorithms and infrastructures capable of uncovering patterns and delivering predictive insights at scale. From enabling autonomous vehicles to powering natural language interfaces, this paradigm transcends traditional machine learning by leveraging multilayer neural networks that adapt and improve over time.

This introduction sets the stage for a comprehensive exploration of how deep learning has evolved, the strategic shifts reshaping its adoption, and the critical factors influencing its trajectory. The subsequent analysis will delve into transformative market dynamics, the ripple effects of U.S. tariffs, segmentation-driven insights, regional variances, competitive landscapes, and prescriptive recommendations. Whether you are an executive evaluating investment priorities or a technology leader planning deployment strategies, understanding these developments is essential to maintaining a competitive edge. With a clear, authoritative perspective, this summary bridges the gap between technical complexity and strategic decision-making, equipping readers with the context and clarity needed to navigate the burgeoning deep learning ecosystem.

Transformative Shifts in the Deep Learning Landscape

Over the past decade, deep learning architectures have transitioned from academic curiosities to commercial imperatives. Convolutional neural networks once confined to image processing now underpin facial recognition, medical diagnostics, and security systems. Generative adversarial networks, initially conceived for synthetic image creation, have unlocked new possibilities in content generation and data augmentation. Recurrent neural networks, once limited by vanishing gradients, have found renewed purpose through innovations like gated recurrent units, driving advances in machine translation and voice assistants.

Meanwhile, hardware breakthroughs-from custom ASICs to tensor processing units-have accelerated model training and inference, slashing development cycles. Software frameworks such as TensorFlow, PyTorch, and MXNet have democratized access to complex algorithms, empowering enterprises to experiment and iterate rapidly. As these technological shifts converge, organizations are reevaluating legacy systems and restructuring workflows to capitalize on deep learning’s potential. In doing so, they confront not only infrastructure challenges but also the need for new talent pipelines, robust data governance, and interdisciplinary collaboration. This section unpacks these transformative dynamics, highlighting how strategic investments and ecosystem maturation are propelling deep learning from proof-of-concept to mission-critical deployments across diverse sectors.

Cumulative Impact of U.S. Tariffs on Deep Learning Supply Chains in 2025

In 2025, the imposition of U.S. tariffs on key deep learning hardware components and specialized chips introduced new complexities for global supply chains. Companies reliant on GPUs, TPUs, and ASICS experienced cost escalations that reverberated across procurement, R&D budgets, and pricing strategies. However, these headwinds also spurred diversification: some manufacturers accelerated partnerships with non-U.S. foundries, while others ramped investments in field-programmable gate arrays to mitigate tariff exposure.

This shift prompted a reevaluation of sourcing strategies, driving a trend toward vertically integrated solutions and localized assembly lines. Research institutions and startups adapted by optimizing neural architectures for efficiency-reducing parameter counts and prioritizing quantization techniques to lower hardware dependencies. Concurrently, strategic alliances between chip designers and cloud providers expanded, offering subscription-based access to specialized accelerators without upfront capital expenditure.

Although tariffs introduced short-term disruptions, they also catalyzed resilience and innovation. By fostering a more distributed supply chain and emphasizing software-hardware co-optimization, the industry has emerged with a more robust architecture designed to adapt to policy fluctuations and geopolitical uncertainties.

Key Segmentation Insights Across Technology, Application, End User, Performance, and Deployment

A nuanced segmentation of the deep learning market reveals critical insights across multiple dimensions. When examining technology, algorithm types such as autoencoders, convolutional neural networks, deep belief networks, generative adversarial networks, and recurrent neural networks each address distinct data challenges-from anomaly detection to sequential modeling-while hardware options including ASICs, FPGAs, GPUs, and TPUs cater to performance and energy trade-offs. Software frameworks like Caffe, Keras, MXNet, PyTorch, and TensorFlow further influence adoption by balancing developer productivity against computational efficiency.

From an application standpoint, autonomous vehicles leverage drones, robotics platforms, and self-driving cars to navigate complex environments; image recognition solutions span facial recognition, object detection, and pattern recognition; natural language processing underpins machine translation, sentiment analysis, and text classification; speech recognition capabilities integrate speech-to-text conversion, voice assistants, and command interfaces. End-user segmentation highlights finance use cases in algorithmic trading, credit scoring, and fraud detection; healthcare applications in medical imaging, personalized medicine, and predictive diagnostics; manufacturing deployments for predictive maintenance, quality control, and supply chain optimization; and retail scenarios involving customer behavior analysis, inventory management, and recommendation systems.

Performance-driven decisions hinge on accuracy metrics such as F1 score, precision, and recall, cost efficiency measured by computational and data processing expenses, scalability across data volume and model complexity, and training time factors like epoch count and processing speed. Deployment scenarios range from cloud-based environments-hybrid, private, and public clouds-to edge devices, including IoT sensors, mobile devices, and wearables, as well as on-premises solutions tailored for enterprise and SMB infrastructures. This holistic segmentation underscores the interplay between technological capabilities and market needs, guiding strategic prioritization.

Key Regional Insights Highlighting Americas, EMEA, and Asia-Pacific Dynamics

Regional dynamics significantly shape deep learning adoption and innovation. In the Americas, robust venture capital ecosystems and leading academic institutions accelerate research commercialization, with North American data centers hosting a large share of global AI workloads. Latin American markets, meanwhile, focus on cost-sensitive deployments in agriculture and resource management, leveraging edge computing to overcome connectivity constraints.

Across Europe, the Middle East, and Africa, regulatory frameworks and data privacy mandates drive investments in on-premises and private cloud architectures. European Union initiatives promote collaborative research hubs, while Middle Eastern economies diversify through smart city and defense applications. African startups harness deep learning for mobile financial services and healthcare diagnostics, often optimizing for low-cost hardware.

In Asia-Pacific, government-led AI strategies in China, Japan, South Korea, and India bolster domestic semiconductor production and expedite national infrastructure upgrades. The region’s high smartphone penetration fuels advanced voice assistant and image recognition services, while emerging markets emphasize localized language models and predictive maintenance in manufacturing sectors. Together, these regional insights reflect a landscape where policy, infrastructure, and cultural factors converge to define unique pathways for deep learning impact.

Key Companies Shaping Deep Learning Innovation and Competition

The competitive landscape is shaped by leading technology and semiconductor firms, each contributing distinct capabilities and strategic initiatives. Advanced Micro Devices, Inc. and NVIDIA Corporation continue to push GPU performance boundaries, while ARM Ltd. empowers a broad ecosystem of low-power inference devices. Intel Corporation and Qualcomm Technologies, Inc. are expanding their portfolios into neural accelerators and heterogeneous computing platforms. Broadcom Corporation and CEVA Inc. specialize in customized signal processing solutions tailored for edge inference.

Software innovators such as Google LLC, Microsoft Corporation, and OpenAI deliver end-to-end AI platforms and pre-trained models that streamline development lifecycles. Huawei Technologies and Samsung Group focus on integrating deep learning within consumer electronics and telecommunication infrastructures, driving high-volume adoption. International Business Machines Corporation leverages its enterprise services expertise to deploy AI solutions in regulated industries, while Clarifai, Inc. and Neurala differentiate through vertical-specific vision and autonomous intelligence applications.

Collectively, these companies engage in strategic partnerships, patent cross-licensing, and open-source contributions to accelerate algorithmic innovation. Their competitive dynamics underscore a market in which hardware prowess, software ecosystems, and go-to-market agility determine leadership and influence the trajectory of deep learning adoption.

Actionable Recommendations for Industry Leaders to Navigate Deep Learning Challenges

To navigate the complex deep learning landscape, industry leaders should adopt a multifaceted strategy. First, align R&D investments with modular architectures that permit rapid iteration of algorithm types and hardware accelerators-this approach balances innovation with cost control. Next, establish supply chain diversification plans that combine cloud-native services with edge deployments, mitigating geopolitical risks and tariff impacts.

Leaders must also foster interdisciplinary talent by partnering with academic institutions and offering specialized training programs that span data science, software engineering, and hardware design. A centralized governance framework should define ethical guidelines and data management protocols, ensuring compliance with evolving privacy regulations. On the commercial front, prioritize vertical integration of deep learning solutions, embedding analytics within existing workflows to demonstrate measurable ROI and accelerate stakeholder buy-in.

Finally, cultivate an ecosystem of strategic alliances-engage with chip manufacturers, cloud providers, and open-source communities to co-develop optimized stacks. By executing these recommendations in parallel, organizations will enhance agility, reduce time-to-value, and build sustainable competitive advantage in a rapidly maturing deep learning market.

Conclusion Emphasizing Strategic Opportunities in Deep Learning

Deep learning’s rapid advancement presents both strategic opportunities and operational complexities. By understanding the macro shifts, tariff influences, and multidimensional segmentation insights, decision-makers can position their organizations to capitalize on next-generation AI applications. Regional analysis highlights the importance of aligning deployment strategies with local policies and infrastructure realities, while competitive profiling underscores where partnerships and innovations drive differentiation.

Looking ahead, the most successful organizations will adopt a holistic approach-integrating modular technology stacks, agile procurement frameworks, and a culture of continuous learning. Emphasizing responsible AI practices will not only address regulatory requirements but also foster trust among end users. Ultimately, the future of deep learning hinges on an organization’s ability to orchestrate people, processes, and technologies in a coherent strategy that adapts to evolving market forces and technological breakthroughs.

Market Segmentation & Coverage

This research report categorizes the Deep Learning Market to forecast the revenues and analyze trends in each of the following sub-segmentations:

  • Algorithm Type
    • Autoencoders
    • Convolutional Neural Networks
    • Deep Belief Networks
    • Generative Adversarial Networks
    • Recurrent Neural Networks
  • Hardware
    • ASICs
    • FPGAs
    • GPUs
    • TPUs
  • Software Frameworks
    • Caffe
    • Keras
    • MXNet
    • PyTorch
    • TensorFlow
  • Autonomous Vehicles
    • Drones
    • Robotics
    • Self-Driving Cars
  • Image Recognition
    • Facial Recognition
    • Object Detection
    • Pattern Recognition
  • Natural Language Processing
    • Machine Translation
    • Sentiment Analysis
    • Text Classification
  • Speech Recognition
    • Speech-To-Text
    • Voice Assistants
    • Voice Command
  • Finance
    • Algorithmic Trading
    • Credit Scoring
    • Fraud Detection
  • Healthcare
    • Medical Imaging
    • Personalized Medicine
    • Predictive Diagnostics
  • Manufacturing
    • Predictive Maintenance
    • Quality Control
    • Supply Chain Optimization
  • Retail
    • Customer Behavior Analysis
    • Inventory Management
    • Recommendation Systems
  • Accuracy
    • F1 Score
    • Precision
    • Recall
  • Cost Efficiency
    • Computational Cost
    • Data Processing Cost
  • Scalability
    • Data Scalability
    • Model Scalability
  • Training Time
    • Epoch Count
    • Processing Speed
  • Cloud-Based
    • Hybrid Cloud
    • Private Cloud
    • Public Cloud
  • Edge Devices
    • IoT Devices
    • Mobile Devices
    • Wearables
  • On-Premises
    • Enterprise Solutions
    • SMB Solutions

This research report categorizes the Deep Learning Market to forecast the revenues and analyze trends in each of the following sub-regions:

  • Americas
    • Argentina
    • Brazil
    • Canada
    • Mexico
    • United States
      • California
      • Florida
      • Illinois
      • New York
      • Ohio
      • Pennsylvania
      • Texas
  • Asia-Pacific
    • Australia
    • China
    • India
    • Indonesia
    • Japan
    • Malaysia
    • Philippines
    • Singapore
    • South Korea
    • Taiwan
    • Thailand
    • Vietnam
  • Europe, Middle East & Africa
    • Denmark
    • Egypt
    • Finland
    • France
    • Germany
    • Israel
    • Italy
    • Netherlands
    • Nigeria
    • Norway
    • Poland
    • Qatar
    • Russia
    • Saudi Arabia
    • South Africa
    • Spain
    • Sweden
    • Switzerland
    • Turkey
    • United Arab Emirates
    • United Kingdom

This research report categorizes the Deep Learning Market to delves into recent significant developments and analyze trends in each of the following companies:

  • Advanced Micro Devices, Inc.
  • ARM Ltd.
  • Broadcom Corporation
  • CEVA Inc.
  • Clarifai, Inc.
  • Google LLC
  • Huawei Technologies
  • Intel Corporation
  • International Business Machines Corporation
  • Microsoft Corporation
  • Neurala
  • NVIDIA Corporation
  • OpenAI
  • Qualcomm Technologies, Inc
  • Samsung Group
  • Starmind

Table of Contents

1. Preface
1.1. Objectives of the Study
1.2. Market Segmentation & Coverage
1.3. Years Considered for the Study
1.4. Currency & Pricing
1.5. Language
1.6. Stakeholders
2. Research Methodology
2.1. Define: Research Objective
2.2. Determine: Research Design
2.3. Prepare: Research Instrument
2.4. Collect: Data Source
2.5. Analyze: Data Interpretation
2.6. Formulate: Data Verification
2.7. Publish: Research Report
2.8. Repeat: Report Update
3. Executive Summary
4. Market Overview
4.1. Introduction
4.2. Market Sizing & Forecasting
5. Market Dynamics
6. Market Insights
6.1. Porter’s Five Forces Analysis
6.2. PESTLE Analysis
7. Cumulative Impact of United States Tariffs 2025
8. Deep Learning Market, by Technology
8.1. Introduction
8.2. Algorithm Type
8.2.1. Autoencoders
8.2.2. Convolutional Neural Networks
8.2.3. Deep Belief Networks
8.2.4. Generative Adversarial Networks
8.2.5. Recurrent Neural Networks
8.3. Hardware
8.3.1. ASICs
8.3.2. FPGAs
8.3.3. GPUs
8.3.4. TPUs
8.4. Software Frameworks
8.4.1. Caffe
8.4.2. Keras
8.4.3. MXNet
8.4.4. PyTorch
8.4.5. TensorFlow
9. Deep Learning Market, by Application
9.1. Introduction
9.2. Autonomous Vehicles
9.2.1. Drones
9.2.2. Robotics
9.2.3. Self-Driving Cars
9.3. Image Recognition
9.3.1. Facial Recognition
9.3.2. Object Detection
9.3.3. Pattern Recognition
9.4. Natural Language Processing
9.4.1. Machine Translation
9.4.2. Sentiment Analysis
9.4.3. Text Classification
9.5. Speech Recognition
9.5.1. Speech-To-Text
9.5.2. Voice Assistants
9.5.3. Voice Command
10. Deep Learning Market, by End User
10.1. Introduction
10.2. Finance
10.2.1. Algorithmic Trading
10.2.2. Credit Scoring
10.2.3. Fraud Detection
10.3. Healthcare
10.3.1. Medical Imaging
10.3.2. Personalized Medicine
10.3.3. Predictive Diagnostics
10.4. Manufacturing
10.4.1. Predictive Maintenance
10.4.2. Quality Control
10.4.3. Supply Chain Optimization
10.5. Retail
10.5.1. Customer Behavior Analysis
10.5.2. Inventory Management
10.5.3. Recommendation Systems
11. Deep Learning Market, by Performance Metrics
11.1. Introduction
11.2. Accuracy
11.2.1. F1 Score
11.2.2. Precision
11.2.3. Recall
11.3. Cost Efficiency
11.3.1. Computational Cost
11.3.2. Data Processing Cost
11.4. Scalability
11.4.1. Data Scalability
11.4.2. Model Scalability
11.5. Training Time
11.5.1. Epoch Count
11.5.2. Processing Speed
12. Deep Learning Market, by Deployment
12.1. Introduction
12.2. Cloud-Based
12.2.1. Hybrid Cloud
12.2.2. Private Cloud
12.2.3. Public Cloud
12.3. Edge Devices
12.3.1. IoT Devices
12.3.2. Mobile Devices
12.3.3. Wearables
12.4. On-Premises
12.4.1. Enterprise Solutions
12.4.2. SMB Solutions
13. Americas Deep Learning Market
13.1. Introduction
13.2. Argentina
13.3. Brazil
13.4. Canada
13.5. Mexico
13.6. United States
14. Asia-Pacific Deep Learning Market
14.1. Introduction
14.2. Australia
14.3. China
14.4. India
14.5. Indonesia
14.6. Japan
14.7. Malaysia
14.8. Philippines
14.9. Singapore
14.10. South Korea
14.11. Taiwan
14.12. Thailand
14.13. Vietnam
15. Europe, Middle East & Africa Deep Learning Market
15.1. Introduction
15.2. Denmark
15.3. Egypt
15.4. Finland
15.5. France
15.6. Germany
15.7. Israel
15.8. Italy
15.9. Netherlands
15.10. Nigeria
15.11. Norway
15.12. Poland
15.13. Qatar
15.14. Russia
15.15. Saudi Arabia
15.16. South Africa
15.17. Spain
15.18. Sweden
15.19. Switzerland
15.20. Turkey
15.21. United Arab Emirates
15.22. United Kingdom
16. Competitive Landscape
16.1. Market Share Analysis, 2024
16.2. FPNV Positioning Matrix, 2024
16.3. Competitive Analysis
16.3.1. Advanced Micro Devices, Inc.
16.3.2. ARM Ltd.
16.3.3. Broadcom Corporation
16.3.4. CEVA Inc.
16.3.5. Clarifai, Inc.
16.3.6. Google LLC
16.3.7. Huawei Technologies
16.3.8. Intel Corporation
16.3.9. International Business Machines Corporation
16.3.10. Microsoft Corporation
16.3.11. Neurala
16.3.12. NVIDIA Corporation
16.3.13. OpenAI
16.3.14. Qualcomm Technologies, Inc
16.3.15. Samsung Group
16.3.16. Starmind
17. ResearchAI
18. ResearchStatistics
19. ResearchContacts
20. ResearchArticles
21. Appendix
List of Figures
FIGURE 1. DEEP LEARNING MARKET MULTI-CURRENCY
FIGURE 2. DEEP LEARNING MARKET MULTI-LANGUAGE
FIGURE 3. DEEP LEARNING MARKET RESEARCH PROCESS
FIGURE 4. GLOBAL DEEP LEARNING MARKET SIZE, 2018-2030 (USD MILLION)
FIGURE 5. GLOBAL DEEP LEARNING MARKET SIZE, BY REGION, 2024 VS 2025 VS 2030 (USD MILLION)
FIGURE 6. GLOBAL DEEP LEARNING MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2030 (USD MILLION)
FIGURE 7. GLOBAL DEEP LEARNING MARKET SIZE, BY TECHNOLOGY, 2024 VS 2030 (%)
FIGURE 8. GLOBAL DEEP LEARNING MARKET SIZE, BY TECHNOLOGY, 2024 VS 2025 VS 2030 (USD MILLION)
FIGURE 9. GLOBAL DEEP LEARNING MARKET SIZE, BY APPLICATION, 2024 VS 2030 (%)
FIGURE 10. GLOBAL DEEP LEARNING MARKET SIZE, BY APPLICATION, 2024 VS 2025 VS 2030 (USD MILLION)
FIGURE 11. GLOBAL DEEP LEARNING MARKET SIZE, BY END USER, 2024 VS 2030 (%)
FIGURE 12. GLOBAL DEEP LEARNING MARKET SIZE, BY END USER, 2024 VS 2025 VS 2030 (USD MILLION)
FIGURE 13. GLOBAL DEEP LEARNING MARKET SIZE, BY PERFORMANCE METRICS, 2024 VS 2030 (%)
FIGURE 14. GLOBAL DEEP LEARNING MARKET SIZE, BY PERFORMANCE METRICS, 2024 VS 2025 VS 2030 (USD MILLION)
FIGURE 15. GLOBAL DEEP LEARNING MARKET SIZE, BY DEPLOYMENT, 2024 VS 2030 (%)
FIGURE 16. GLOBAL DEEP LEARNING MARKET SIZE, BY DEPLOYMENT, 2024 VS 2025 VS 2030 (USD MILLION)
FIGURE 17. AMERICAS DEEP LEARNING MARKET SIZE, BY COUNTRY, 2024 VS 2030 (%)
FIGURE 18. AMERICAS DEEP LEARNING MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2030 (USD MILLION)
FIGURE 19. UNITED STATES DEEP LEARNING MARKET SIZE, BY STATE, 2024 VS 2030 (%)
FIGURE 20. UNITED STATES DEEP LEARNING MARKET SIZE, BY STATE, 2024 VS 2025 VS 2030 (USD MILLION)
FIGURE 21. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY COUNTRY, 2024 VS 2030 (%)
FIGURE 22. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2030 (USD MILLION)
FIGURE 23. EUROPE, MIDDLE EAST & AFRICA DEEP LEARNING MARKET SIZE, BY COUNTRY, 2024 VS 2030 (%)
FIGURE 24. EUROPE, MIDDLE EAST & AFRICA DEEP LEARNING MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2030 (USD MILLION)
FIGURE 25. DEEP LEARNING MARKET SHARE, BY KEY PLAYER, 2024
FIGURE 26. DEEP LEARNING MARKET, FPNV POSITIONING MATRIX, 2024
List of Tables
TABLE 1. DEEP LEARNING MARKET SEGMENTATION & COVERAGE
TABLE 2. UNITED STATES DOLLAR EXCHANGE RATE, 2018-2024
TABLE 3. GLOBAL DEEP LEARNING MARKET SIZE, 2018-2030 (USD MILLION)
TABLE 4. GLOBAL DEEP LEARNING MARKET SIZE, BY REGION, 2018-2030 (USD MILLION)
TABLE 5. GLOBAL DEEP LEARNING MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
TABLE 6. GLOBAL DEEP LEARNING MARKET SIZE, BY TECHNOLOGY, 2018-2030 (USD MILLION)
TABLE 7. GLOBAL DEEP LEARNING MARKET SIZE, BY ALGORITHM TYPE, BY REGION, 2018-2030 (USD MILLION)
TABLE 8. GLOBAL DEEP LEARNING MARKET SIZE, BY AUTOENCODERS, BY REGION, 2018-2030 (USD MILLION)
TABLE 9. GLOBAL DEEP LEARNING MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY REGION, 2018-2030 (USD MILLION)
TABLE 10. GLOBAL DEEP LEARNING MARKET SIZE, BY DEEP BELIEF NETWORKS, BY REGION, 2018-2030 (USD MILLION)
TABLE 11. GLOBAL DEEP LEARNING MARKET SIZE, BY GENERATIVE ADVERSARIAL NETWORKS, BY REGION, 2018-2030 (USD MILLION)
TABLE 12. GLOBAL DEEP LEARNING MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY REGION, 2018-2030 (USD MILLION)
TABLE 13. GLOBAL DEEP LEARNING MARKET SIZE, BY ALGORITHM TYPE, 2018-2030 (USD MILLION)
TABLE 14. GLOBAL DEEP LEARNING MARKET SIZE, BY HARDWARE, BY REGION, 2018-2030 (USD MILLION)
TABLE 15. GLOBAL DEEP LEARNING MARKET SIZE, BY ASICS, BY REGION, 2018-2030 (USD MILLION)
TABLE 16. GLOBAL DEEP LEARNING MARKET SIZE, BY FPGAS, BY REGION, 2018-2030 (USD MILLION)
TABLE 17. GLOBAL DEEP LEARNING MARKET SIZE, BY GPUS, BY REGION, 2018-2030 (USD MILLION)
TABLE 18. GLOBAL DEEP LEARNING MARKET SIZE, BY TPUS, BY REGION, 2018-2030 (USD MILLION)
TABLE 19. GLOBAL DEEP LEARNING MARKET SIZE, BY HARDWARE, 2018-2030 (USD MILLION)
TABLE 20. GLOBAL DEEP LEARNING MARKET SIZE, BY SOFTWARE FRAMEWORKS, BY REGION, 2018-2030 (USD MILLION)
TABLE 21. GLOBAL DEEP LEARNING MARKET SIZE, BY CAFFE, BY REGION, 2018-2030 (USD MILLION)
TABLE 22. GLOBAL DEEP LEARNING MARKET SIZE, BY KERAS, BY REGION, 2018-2030 (USD MILLION)
TABLE 23. GLOBAL DEEP LEARNING MARKET SIZE, BY MXNET, BY REGION, 2018-2030 (USD MILLION)
TABLE 24. GLOBAL DEEP LEARNING MARKET SIZE, BY PYTORCH, BY REGION, 2018-2030 (USD MILLION)
TABLE 25. GLOBAL DEEP LEARNING MARKET SIZE, BY TENSORFLOW, BY REGION, 2018-2030 (USD MILLION)
TABLE 26. GLOBAL DEEP LEARNING MARKET SIZE, BY SOFTWARE FRAMEWORKS, 2018-2030 (USD MILLION)
TABLE 27. GLOBAL DEEP LEARNING MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
TABLE 28. GLOBAL DEEP LEARNING MARKET SIZE, BY AUTONOMOUS VEHICLES, BY REGION, 2018-2030 (USD MILLION)
TABLE 29. GLOBAL DEEP LEARNING MARKET SIZE, BY DRONES, BY REGION, 2018-2030 (USD MILLION)
TABLE 30. GLOBAL DEEP LEARNING MARKET SIZE, BY ROBOTICS, BY REGION, 2018-2030 (USD MILLION)
TABLE 31. GLOBAL DEEP LEARNING MARKET SIZE, BY SELF-DRIVING CARS, BY REGION, 2018-2030 (USD MILLION)
TABLE 32. GLOBAL DEEP LEARNING MARKET SIZE, BY AUTONOMOUS VEHICLES, 2018-2030 (USD MILLION)
TABLE 33. GLOBAL DEEP LEARNING MARKET SIZE, BY IMAGE RECOGNITION, BY REGION, 2018-2030 (USD MILLION)
TABLE 34. GLOBAL DEEP LEARNING MARKET SIZE, BY FACIAL RECOGNITION, BY REGION, 2018-2030 (USD MILLION)
TABLE 35. GLOBAL DEEP LEARNING MARKET SIZE, BY OBJECT DETECTION, BY REGION, 2018-2030 (USD MILLION)
TABLE 36. GLOBAL DEEP LEARNING MARKET SIZE, BY PATTERN RECOGNITION, BY REGION, 2018-2030 (USD MILLION)
TABLE 37. GLOBAL DEEP LEARNING MARKET SIZE, BY IMAGE RECOGNITION, 2018-2030 (USD MILLION)
TABLE 38. GLOBAL DEEP LEARNING MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY REGION, 2018-2030 (USD MILLION)
TABLE 39. GLOBAL DEEP LEARNING MARKET SIZE, BY MACHINE TRANSLATION, BY REGION, 2018-2030 (USD MILLION)
TABLE 40. GLOBAL DEEP LEARNING MARKET SIZE, BY SENTIMENT ANALYSIS, BY REGION, 2018-2030 (USD MILLION)
TABLE 41. GLOBAL DEEP LEARNING MARKET SIZE, BY TEXT CLASSIFICATION, BY REGION, 2018-2030 (USD MILLION)
TABLE 42. GLOBAL DEEP LEARNING MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2018-2030 (USD MILLION)
TABLE 43. GLOBAL DEEP LEARNING MARKET SIZE, BY SPEECH RECOGNITION, BY REGION, 2018-2030 (USD MILLION)
TABLE 44. GLOBAL DEEP LEARNING MARKET SIZE, BY SPEECH-TO-TEXT, BY REGION, 2018-2030 (USD MILLION)
TABLE 45. GLOBAL DEEP LEARNING MARKET SIZE, BY VOICE ASSISTANTS, BY REGION, 2018-2030 (USD MILLION)
TABLE 46. GLOBAL DEEP LEARNING MARKET SIZE, BY VOICE COMMAND, BY REGION, 2018-2030 (USD MILLION)
TABLE 47. GLOBAL DEEP LEARNING MARKET SIZE, BY SPEECH RECOGNITION, 2018-2030 (USD MILLION)
TABLE 48. GLOBAL DEEP LEARNING MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
TABLE 49. GLOBAL DEEP LEARNING MARKET SIZE, BY FINANCE, BY REGION, 2018-2030 (USD MILLION)
TABLE 50. GLOBAL DEEP LEARNING MARKET SIZE, BY ALGORITHMIC TRADING, BY REGION, 2018-2030 (USD MILLION)
TABLE 51. GLOBAL DEEP LEARNING MARKET SIZE, BY CREDIT SCORING, BY REGION, 2018-2030 (USD MILLION)
TABLE 52. GLOBAL DEEP LEARNING MARKET SIZE, BY FRAUD DETECTION, BY REGION, 2018-2030 (USD MILLION)
TABLE 53. GLOBAL DEEP LEARNING MARKET SIZE, BY FINANCE, 2018-2030 (USD MILLION)
TABLE 54. GLOBAL DEEP LEARNING MARKET SIZE, BY HEALTHCARE, BY REGION, 2018-2030 (USD MILLION)
TABLE 55. GLOBAL DEEP LEARNING MARKET SIZE, BY MEDICAL IMAGING, BY REGION, 2018-2030 (USD MILLION)
TABLE 56. GLOBAL DEEP LEARNING MARKET SIZE, BY PERSONALIZED MEDICINE, BY REGION, 2018-2030 (USD MILLION)
TABLE 57. GLOBAL DEEP LEARNING MARKET SIZE, BY PREDICTIVE DIAGNOSTICS, BY REGION, 2018-2030 (USD MILLION)
TABLE 58. GLOBAL DEEP LEARNING MARKET SIZE, BY HEALTHCARE, 2018-2030 (USD MILLION)
TABLE 59. GLOBAL DEEP LEARNING MARKET SIZE, BY MANUFACTURING, BY REGION, 2018-2030 (USD MILLION)
TABLE 60. GLOBAL DEEP LEARNING MARKET SIZE, BY PREDICTIVE MAINTENANCE, BY REGION, 2018-2030 (USD MILLION)
TABLE 61. GLOBAL DEEP LEARNING MARKET SIZE, BY QUALITY CONTROL, BY REGION, 2018-2030 (USD MILLION)
TABLE 62. GLOBAL DEEP LEARNING MARKET SIZE, BY SUPPLY CHAIN OPTIMIZATION, BY REGION, 2018-2030 (USD MILLION)
TABLE 63. GLOBAL DEEP LEARNING MARKET SIZE, BY MANUFACTURING, 2018-2030 (USD MILLION)
TABLE 64. GLOBAL DEEP LEARNING MARKET SIZE, BY RETAIL, BY REGION, 2018-2030 (USD MILLION)
TABLE 65. GLOBAL DEEP LEARNING MARKET SIZE, BY CUSTOMER BEHAVIOR ANALYSIS, BY REGION, 2018-2030 (USD MILLION)
TABLE 66. GLOBAL DEEP LEARNING MARKET SIZE, BY INVENTORY MANAGEMENT, BY REGION, 2018-2030 (USD MILLION)
TABLE 67. GLOBAL DEEP LEARNING MARKET SIZE, BY RECOMMENDATION SYSTEMS, BY REGION, 2018-2030 (USD MILLION)
TABLE 68. GLOBAL DEEP LEARNING MARKET SIZE, BY RETAIL, 2018-2030 (USD MILLION)
TABLE 69. GLOBAL DEEP LEARNING MARKET SIZE, BY PERFORMANCE METRICS, 2018-2030 (USD MILLION)
TABLE 70. GLOBAL DEEP LEARNING MARKET SIZE, BY ACCURACY, BY REGION, 2018-2030 (USD MILLION)
TABLE 71. GLOBAL DEEP LEARNING MARKET SIZE, BY F1 SCORE, BY REGION, 2018-2030 (USD MILLION)
TABLE 72. GLOBAL DEEP LEARNING MARKET SIZE, BY PRECISION, BY REGION, 2018-2030 (USD MILLION)
TABLE 73. GLOBAL DEEP LEARNING MARKET SIZE, BY RECALL, BY REGION, 2018-2030 (USD MILLION)
TABLE 74. GLOBAL DEEP LEARNING MARKET SIZE, BY ACCURACY, 2018-2030 (USD MILLION)
TABLE 75. GLOBAL DEEP LEARNING MARKET SIZE, BY COST EFFICIENCY, BY REGION, 2018-2030 (USD MILLION)
TABLE 76. GLOBAL DEEP LEARNING MARKET SIZE, BY COMPUTATIONAL COST, BY REGION, 2018-2030 (USD MILLION)
TABLE 77. GLOBAL DEEP LEARNING MARKET SIZE, BY DATA PROCESSING COST, BY REGION, 2018-2030 (USD MILLION)
TABLE 78. GLOBAL DEEP LEARNING MARKET SIZE, BY COST EFFICIENCY, 2018-2030 (USD MILLION)
TABLE 79. GLOBAL DEEP LEARNING MARKET SIZE, BY SCALABILITY, BY REGION, 2018-2030 (USD MILLION)
TABLE 80. GLOBAL DEEP LEARNING MARKET SIZE, BY DATA SCALABILITY, BY REGION, 2018-2030 (USD MILLION)
TABLE 81. GLOBAL DEEP LEARNING MARKET SIZE, BY MODEL SCALABILITY, BY REGION, 2018-2030 (USD MILLION)
TABLE 82. GLOBAL DEEP LEARNING MARKET SIZE, BY SCALABILITY, 2018-2030 (USD MILLION)
TABLE 83. GLOBAL DEEP LEARNING MARKET SIZE, BY TRAINING TIME, BY REGION, 2018-2030 (USD MILLION)
TABLE 84. GLOBAL DEEP LEARNING MARKET SIZE, BY EPOCH COUNT, BY REGION, 2018-2030 (USD MILLION)
TABLE 85. GLOBAL DEEP LEARNING MARKET SIZE, BY PROCESSING SPEED, BY REGION, 2018-2030 (USD MILLION)
TABLE 86. GLOBAL DEEP LEARNING MARKET SIZE, BY TRAINING TIME, 2018-2030 (USD MILLION)
TABLE 87. GLOBAL DEEP LEARNING MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
TABLE 88. GLOBAL DEEP LEARNING MARKET SIZE, BY CLOUD-BASED, BY REGION, 2018-2030 (USD MILLION)
TABLE 89. GLOBAL DEEP LEARNING MARKET SIZE, BY HYBRID CLOUD, BY REGION, 2018-2030 (USD MILLION)
TABLE 90. GLOBAL DEEP LEARNING MARKET SIZE, BY PRIVATE CLOUD, BY REGION, 2018-2030 (USD MILLION)
TABLE 91. GLOBAL DEEP LEARNING MARKET SIZE, BY PUBLIC CLOUD, BY REGION, 2018-2030 (USD MILLION)
TABLE 92. GLOBAL DEEP LEARNING MARKET SIZE, BY CLOUD-BASED, 2018-2030 (USD MILLION)
TABLE 93. GLOBAL DEEP LEARNING MARKET SIZE, BY EDGE DEVICES, BY REGION, 2018-2030 (USD MILLION)
TABLE 94. GLOBAL DEEP LEARNING MARKET SIZE, BY IOT DEVICES, BY REGION, 2018-2030 (USD MILLION)
TABLE 95. GLOBAL DEEP LEARNING MARKET SIZE, BY MOBILE DEVICES, BY REGION, 2018-2030 (USD MILLION)
TABLE 96. GLOBAL DEEP LEARNING MARKET SIZE, BY WEARABLES, BY REGION, 2018-2030 (USD MILLION)
TABLE 97. GLOBAL DEEP LEARNING MARKET SIZE, BY EDGE DEVICES, 2018-2030 (USD MILLION)
TABLE 98. GLOBAL DEEP LEARNING MARKET SIZE, BY ON-PREMISES, BY REGION, 2018-2030 (USD MILLION)
TABLE 99. GLOBAL DEEP LEARNING MARKET SIZE, BY ENTERPRISE SOLUTIONS, BY REGION, 2018-2030 (USD MILLION)
TABLE 100. GLOBAL DEEP LEARNING MARKET SIZE, BY SMB SOLUTIONS, BY REGION, 2018-2030 (USD MILLION)
TABLE 101. GLOBAL DEEP LEARNING MARKET SIZE, BY ON-PREMISES, 2018-2030 (USD MILLION)
TABLE 102. AMERICAS DEEP LEARNING MARKET SIZE, BY TECHNOLOGY, 2018-2030 (USD MILLION)
TABLE 103. AMERICAS DEEP LEARNING MARKET SIZE, BY ALGORITHM TYPE, 2018-2030 (USD MILLION)
TABLE 104. AMERICAS DEEP LEARNING MARKET SIZE, BY HARDWARE, 2018-2030 (USD MILLION)
TABLE 105. AMERICAS DEEP LEARNING MARKET SIZE, BY SOFTWARE FRAMEWORKS, 2018-2030 (USD MILLION)
TABLE 106. AMERICAS DEEP LEARNING MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
TABLE 107. AMERICAS DEEP LEARNING MARKET SIZE, BY AUTONOMOUS VEHICLES, 2018-2030 (USD MILLION)
TABLE 108. AMERICAS DEEP LEARNING MARKET SIZE, BY IMAGE RECOGNITION, 2018-2030 (USD MILLION)
TABLE 109. AMERICAS DEEP LEARNING MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2018-2030 (USD MILLION)
TABLE 110. AMERICAS DEEP LEARNING MARKET SIZE, BY SPEECH RECOGNITION, 2018-2030 (USD MILLION)
TABLE 111. AMERICAS DEEP LEARNING MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
TABLE 112. AMERICAS DEEP LEARNING MARKET SIZE, BY FINANCE, 2018-2030 (USD MILLION)
TABLE 113. AMERICAS DEEP LEARNING MARKET SIZE, BY HEALTHCARE, 2018-2030 (USD MILLION)
TABLE 114. AMERICAS DEEP LEARNING MARKET SIZE, BY MANUFACTURING, 2018-2030 (USD MILLION)
TABLE 115. AMERICAS DEEP LEARNING MARKET SIZE, BY RETAIL, 2018-2030 (USD MILLION)
TABLE 116. AMERICAS DEEP LEARNING MARKET SIZE, BY PERFORMANCE METRICS, 2018-2030 (USD MILLION)
TABLE 117. AMERICAS DEEP LEARNING MARKET SIZE, BY ACCURACY, 2018-2030 (USD MILLION)
TABLE 118. AMERICAS DEEP LEARNING MARKET SIZE, BY COST EFFICIENCY, 2018-2030 (USD MILLION)
TABLE 119. AMERICAS DEEP LEARNING MARKET SIZE, BY SCALABILITY, 2018-2030 (USD MILLION)
TABLE 120. AMERICAS DEEP LEARNING MARKET SIZE, BY TRAINING TIME, 2018-2030 (USD MILLION)
TABLE 121. AMERICAS DEEP LEARNING MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
TABLE 122. AMERICAS DEEP LEARNING MARKET SIZE, BY CLOUD-BASED, 2018-2030 (USD MILLION)
TABLE 123. AMERICAS DEEP LEARNING MARKET SIZE, BY EDGE DEVICES, 2018-2030 (USD MILLION)
TABLE 124. AMERICAS DEEP LEARNING MARKET SIZE, BY ON-PREMISES, 2018-2030 (USD MILLION)
TABLE 125. AMERICAS DEEP LEARNING MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
TABLE 126. ARGENTINA DEEP LEARNING MARKET SIZE, BY TECHNOLOGY, 2018-2030 (USD MILLION)
TABLE 127. ARGENTINA DEEP LEARNING MARKET SIZE, BY ALGORITHM TYPE, 2018-2030 (USD MILLION)
TABLE 128. ARGENTINA DEEP LEARNING MARKET SIZE, BY HARDWARE, 2018-2030 (USD MILLION)
TABLE 129. ARGENTINA DEEP LEARNING MARKET SIZE, BY SOFTWARE FRAMEWORKS, 2018-2030 (USD MILLION)
TABLE 130. ARGENTINA DEEP LEARNING MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
TABLE 131. ARGENTINA DEEP LEARNING MARKET SIZE, BY AUTONOMOUS VEHICLES, 2018-2030 (USD MILLION)
TABLE 132. ARGENTINA DEEP LEARNING MARKET SIZE, BY IMAGE RECOGNITION, 2018-2030 (USD MILLION)
TABLE 133. ARGENTINA DEEP LEARNING MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2018-2030 (USD MILLION)
TABLE 134. ARGENTINA DEEP LEARNING MARKET SIZE, BY SPEECH RECOGNITION, 2018-2030 (USD MILLION)
TABLE 135. ARGENTINA DEEP LEARNING MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
TABLE 136. ARGENTINA DEEP LEARNING MARKET SIZE, BY FINANCE, 2018-2030 (USD MILLION)
TABLE 137. ARGENTINA DEEP LEARNING MARKET SIZE, BY HEALTHCARE, 2018-2030 (USD MILLION)
TABLE 138. ARGENTINA DEEP LEARNING MARKET SIZE, BY MANUFACTURING, 2018-2030 (USD MILLION)
TABLE 139. ARGENTINA DEEP LEARNING MARKET SIZE, BY RETAIL, 2018-2030 (USD MILLION)
TABLE 140. ARGENTINA DEEP LEARNING MARKET SIZE, BY PERFORMANCE METRICS, 2018-2030 (USD MILLION)
TABLE 141. ARGENTINA DEEP LEARNING MARKET SIZE, BY ACCURACY, 2018-2030 (USD MILLION)
TABLE 142. ARGENTINA DEEP LEARNING MARKET SIZE, BY COST EFFICIENCY, 2018-2030 (USD MILLION)
TABLE 143. ARGENTINA DEEP LEARNING MARKET SIZE, BY SCALABILITY, 2018-2030 (USD MILLION)
TABLE 144. ARGENTINA DEEP LEARNING MARKET SIZE, BY TRAINING TIME, 2018-2030 (USD MILLION)
TABLE 145. ARGENTINA DEEP LEARNING MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
TABLE 146. ARGENTINA DEEP LEARNING MARKET SIZE, BY CLOUD-BASED, 2018-2030 (USD MILLION)
TABLE 147. ARGENTINA DEEP LEARNING MARKET SIZE, BY EDGE DEVICES, 2018-2030 (USD MILLION)
TABLE 148. ARGENTINA DEEP LEARNING MARKET SIZE, BY ON-PREMISES, 2018-2030 (USD MILLION)
TABLE 149. BRAZIL DEEP LEARNING MARKET SIZE, BY TECHNOLOGY, 2018-2030 (USD MILLION)
TABLE 150. BRAZIL DEEP LEARNING MARKET SIZE, BY ALGORITHM TYPE, 2018-2030 (USD MILLION)
TABLE 151. BRAZIL DEEP LEARNING MARKET SIZE, BY HARDWARE, 2018-2030 (USD MILLION)
TABLE 152. BRAZIL DEEP LEARNING MARKET SIZE, BY SOFTWARE FRAMEWORKS, 2018-2030 (USD MILLION)
TABLE 153. BRAZIL DEEP LEARNING MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
TABLE 154. BRAZIL DEEP LEARNING MARKET SIZE, BY AUTONOMOUS VEHICLES, 2018-2030 (USD MILLION)
TABLE 155. BRAZIL DEEP LEARNING MARKET SIZE, BY IMAGE RECOGNITION, 2018-2030 (USD MILLION)
TABLE 156. BRAZIL DEEP LEARNING MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2018-2030 (USD MILLION)
TABLE 157. BRAZIL DEEP LEARNING MARKET SIZE, BY SPEECH RECOGNITION, 2018-2030 (USD MILLION)
TABLE 158. BRAZIL DEEP LEARNING MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
TABLE 159. BRAZIL DEEP LEARNING MARKET SIZE, BY FINANCE, 2018-2030 (USD MILLION)
TABLE 160. BRAZIL DEEP LEARNING MARKET SIZE, BY HEALTHCARE, 2018-2030 (USD MILLION)
TABLE 161. BRAZIL DEEP LEARNING MARKET SIZE, BY MANUFACTURING, 2018-2030 (USD MILLION)
TABLE 162. BRAZIL DEEP LEARNING MARKET SIZE, BY RETAIL, 2018-2030 (USD MILLION)
TABLE 163. BRAZIL DEEP LEARNING MARKET SIZE, BY PERFORMANCE METRICS, 2018-2030 (USD MILLION)
TABLE 164. BRAZIL DEEP LEARNING MARKET SIZE, BY ACCURACY, 2018-2030 (USD MILLION)
TABLE 165. BRAZIL DEEP LEARNING MARKET SIZE, BY COST EFFICIENCY, 2018-2030 (USD MILLION)
TABLE 166. BRAZIL DEEP LEARNING MARKET SIZE, BY SCALABILITY, 2018-2030 (USD MILLION)
TABLE 167. BRAZIL DEEP LEARNING MARKET SIZE, BY TRAINING TIME, 2018-2030 (USD MILLION)
TABLE 168. BRAZIL DEEP LEARNING MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
TABLE 169. BRAZIL DEEP LEARNING MARKET SIZE, BY CLOUD-BASED, 2018-2030 (USD MILLION)
TABLE 170. BRAZIL DEEP LEARNING MARKET SIZE, BY EDGE DEVICES, 2018-2030 (USD MILLION)
TABLE 171. BRAZIL DEEP LEARNING MARKET SIZE, BY ON-PREMISES, 2018-2030 (USD MILLION)
TABLE 172. CANADA DEEP LEARNING MARKET SIZE, BY TECHNOLOGY, 2018-2030 (USD MILLION)
TABLE 173. CANADA DEEP LEARNING MARKET SIZE, BY ALGORITHM TYPE, 2018-2030 (USD MILLION)
TABLE 174. CANADA DEEP LEARNING MARKET SIZE, BY HARDWARE, 2018-2030 (USD MILLION)
TABLE 175. CANADA DEEP LEARNING MARKET SIZE, BY SOFTWARE FRAMEWORKS, 2018-2030 (USD MILLION)
TABLE 176. CANADA DEEP LEARNING MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
TABLE 177. CANADA DEEP LEARNING MARKET SIZE, BY AUTONOMOUS VEHICLES, 2018-2030 (USD MILLION)
TABLE 178. CANADA DEEP LEARNING MARKET SIZE, BY IMAGE RECOGNITION, 2018-2030 (USD MILLION)
TABLE 179. CANADA DEEP LEARNING MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2018-2030 (USD MILLION)
TABLE 180. CANADA DEEP LEARNING MARKET SIZE, BY SPEECH RECOGNITION, 2018-2030 (USD MILLION)
TABLE 181. CANADA DEEP LEARNING MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
TABLE 182. CANADA DEEP LEARNING MARKET SIZE, BY FINANCE, 2018-2030 (USD MILLION)
TABLE 183. CANADA DEEP LEARNING MARKET SIZE, BY HEALTHCARE, 2018-2030 (USD MILLION)
TABLE 184. CANADA DEEP LEARNING MARKET SIZE, BY MANUFACTURING, 2018-2030 (USD MILLION)
TABLE 185. CANADA DEEP LEARNING MARKET SIZE, BY RETAIL, 2018-2030 (USD MILLION)
TABLE 186. CANADA DEEP LEARNING MARKET SIZE, BY PERFORMANCE METRICS, 2018-2030 (USD MILLION)
TABLE 187. CANADA DEEP LEARNING MARKET SIZE, BY ACCURACY, 2018-2030 (USD MILLION)
TABLE 188. CANADA DEEP LEARNING MARKET SIZE, BY COST EFFICIENCY, 2018-2030 (USD MILLION)
TABLE 189. CANADA DEEP LEARNING MARKET SIZE, BY SCALABILITY, 2018-2030 (USD MILLION)
TABLE 190. CANADA DEEP LEARNING MARKET SIZE, BY TRAINING TIME, 2018-2030 (USD MILLION)
TABLE 191. CANADA DEEP LEARNING MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
TABLE 192. CANADA DEEP LEARNING MARKET SIZE, BY CLOUD-BASED, 2018-2030 (USD MILLION)
TABLE 193. CANADA DEEP LEARNING MARKET SIZE, BY EDGE DEVICES, 2018-2030 (USD MILLION)
TABLE 194. CANADA DEEP LEARNING MARKET SIZE, BY ON-PREMISES, 2018-2030 (USD MILLION)
TABLE 195. MEXICO DEEP LEARNING MARKET SIZE, BY TECHNOLOGY, 2018-2030 (USD MILLION)
TABLE 196. MEXICO DEEP LEARNING MARKET SIZE, BY ALGORITHM TYPE, 2018-2030 (USD MILLION)
TABLE 197. MEXICO DEEP LEARNING MARKET SIZE, BY HARDWARE, 2018-2030 (USD MILLION)
TABLE 198. MEXICO DEEP LEARNING MARKET SIZE, BY SOFTWARE FRAMEWORKS, 2018-2030 (USD MILLION)
TABLE 199. MEXICO DEEP LEARNING MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
TABLE 200. MEXICO DEEP LEARNING MARKET SIZE, BY AUTONOMOUS VEHICLES, 2018-2030 (USD MILLION)
TABLE 201. MEXICO DEEP LEARNING MARKET SIZE, BY IMAGE RECOGNITION, 2018-2030 (USD MILLION)
TABLE 202. MEXICO DEEP LEARNING MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2018-2030 (USD MILLION)
TABLE 203. MEXICO DEEP LEARNING MARKET SIZE, BY SPEECH RECOGNITION, 2018-2030 (USD MILLION)
TABLE 204. MEXICO DEEP LEARNING MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
TABLE 205. MEXICO DEEP LEARNING MARKET SIZE, BY FINANCE, 2018-2030 (USD MILLION)
TABLE 206. MEXICO DEEP LEARNING MARKET SIZE, BY HEALTHCARE, 2018-2030 (USD MILLION)
TABLE 207. MEXICO DEEP LEARNING MARKET SIZE, BY MANUFACTURING, 2018-2030 (USD MILLION)
TABLE 208. MEXICO DEEP LEARNING MARKET SIZE, BY RETAIL, 2018-2030 (USD MILLION)
TABLE 209. MEXICO DEEP LEARNING MARKET SIZE, BY PERFORMANCE METRICS, 2018-2030 (USD MILLION)
TABLE 210. MEXICO DEEP LEARNING MARKET SIZE, BY ACCURACY, 2018-2030 (USD MILLION)
TABLE 211. MEXICO DEEP LEARNING MARKET SIZE, BY COST EFFICIENCY, 2018-2030 (USD MILLION)
TABLE 212. MEXICO DEEP LEARNING MARKET SIZE, BY SCALABILITY, 2018-2030 (USD MILLION)
TABLE 213. MEXICO DEEP LEARNING MARKET SIZE, BY TRAINING TIME, 2018-2030 (USD MILLION)
TABLE 214. MEXICO DEEP LEARNING MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
TABLE 215. MEXICO DEEP LEARNING MARKET SIZE, BY CLOUD-BASED, 2018-2030 (USD MILLION)
TABLE 216. MEXICO DEEP LEARNING MARKET SIZE, BY EDGE DEVICES, 2018-2030 (USD MILLION)
TABLE 217. MEXICO DEEP LEARNING MARKET SIZE, BY ON-PREMISES, 2018-2030 (USD MILLION)
TABLE 218. UNITED STATES DEEP LEARNING MARKET SIZE, BY TECHNOLOGY, 2018-2030 (USD MILLION)
TABLE 219. UNITED STATES DEEP LEARNING MARKET SIZE, BY ALGORITHM TYPE, 2018-2030 (USD MILLION)
TABLE 220. UNITED STATES DEEP LEARNING MARKET SIZE, BY HARDWARE, 2018-2030 (USD MILLION)
TABLE 221. UNITED STATES DEEP LEARNING MARKET SIZE, BY SOFTWARE FRAMEWORKS, 2018-2030 (USD MILLION)
TABLE 222. UNITED STATES DEEP LEARNING MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
TABLE 223. UNITED STATES DEEP LEARNING MARKET SIZE, BY AUTONOMOUS VEHICLES, 2018-2030 (USD MILLION)
TABLE 224. UNITED STATES DEEP LEARNING MARKET SIZE, BY IMAGE RECOGNITION, 2018-2030 (USD MILLION)
TABLE 225. UNITED STATES DEEP LEARNING MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2018-2030 (USD MILLION)
TABLE 226. UNITED STATES DEEP LEARNING MARKET SIZE, BY SPEECH RECOGNITION, 2018-2030 (USD MILLION)
TABLE 227. UNITED STATES DEEP LEARNING MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
TABLE 228. UNITED STATES DEEP LEARNING MARKET SIZE, BY FINANCE, 2018-2030 (USD MILLION)
TABLE 229. UNITED STATES DEEP LEARNING MARKET SIZE, BY HEALTHCARE, 2018-2030 (USD MILLION)
TABLE 230. UNITED STATES DEEP LEARNING MARKET SIZE, BY MANUFACTURING, 2018-2030 (USD MILLION)
TABLE 231. UNITED STATES DEEP LEARNING MARKET SIZE, BY RETAIL, 2018-2030 (USD MILLION)
TABLE 232. UNITED STATES DEEP LEARNING MARKET SIZE, BY PERFORMANCE METRICS, 2018-2030 (USD MILLION)
TABLE 233. UNITED STATES DEEP LEARNING MARKET SIZE, BY ACCURACY, 2018-2030 (USD MILLION)
TABLE 234. UNITED STATES DEEP LEARNING MARKET SIZE, BY COST EFFICIENCY, 2018-2030 (USD MILLION)
TABLE 235. UNITED STATES DEEP LEARNING MARKET SIZE, BY SCALABILITY, 2018-2030 (USD MILLION)
TABLE 236. UNITED STATES DEEP LEARNING MARKET SIZE, BY TRAINING TIME, 2018-2030 (USD MILLION)
TABLE 237. UNITED STATES DEEP LEARNING MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
TABLE 238. UNITED STATES DEEP LEARNING MARKET SIZE, BY CLOUD-BASED, 2018-2030 (USD MILLION)
TABLE 239. UNITED STATES DEEP LEARNING MARKET SIZE, BY EDGE DEVICES, 2018-2030 (USD MILLION)
TABLE 240. UNITED STATES DEEP LEARNING MARKET SIZE, BY ON-PREMISES, 2018-2030 (USD MILLION)
TABLE 241. UNITED STATES DEEP LEARNING MARKET SIZE, BY STATE, 2018-2030 (USD MILLION)
TABLE 242. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY TECHNOLOGY, 2018-2030 (USD MILLION)
TABLE 243. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY ALGORITHM TYPE, 2018-2030 (USD MILLION)
TABLE 244. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY HARDWARE, 2018-2030 (USD MILLION)
TABLE 245. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY SOFTWARE FRAMEWORKS, 2018-2030 (USD MILLION)
TABLE 246. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
TABLE 247. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY AUTONOMOUS VEHICLES, 2018-2030 (USD MILLION)
TABLE 248. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY IMAGE RECOGNITION, 2018-2030 (USD MILLION)
TABLE 249. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2018-2030 (USD MILLION)
TABLE 250. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY SPEECH RECOGNITION, 2018-2030 (USD MILLION)
TABLE 251. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
TABLE 252. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY FINANCE, 2018-2030 (USD MILLION)
TABLE 253. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY HEALTHCARE, 2018-2030 (USD MILLION)
TABLE 254. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY MANUFACTURING, 2018-2030 (USD MILLION)
TABLE 255. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY RETAIL, 2018-2030 (USD MILLION)
TABLE 256. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY PERFORMANCE METRICS, 2018-2030 (USD MILLION)
TABLE 257. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY ACCURACY, 2018-2030 (USD MILLION)
TABLE 258. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY COST EFFICIENCY, 2018-2030 (USD MILLION)
TABLE 259. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY SCALABILITY, 2018-2030 (USD MILLION)
TABLE 260. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY TRAINING TIME, 2018-2030 (USD MILLION)
TABLE 261. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
TABLE 262. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY CLOUD-BASED, 2018-2030 (USD MILLION)
TABLE 263. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY EDGE DEVICES, 2018-2030 (USD MILLION)
TABLE 264. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY ON-PREMISES, 2018-2030 (USD MILLION)
TABLE 265. ASIA-PACIFIC DEEP LEARNING MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
TABLE 266. AUSTRALIA DEEP LEARNING MARKET SIZE, BY TECHNOLOGY, 2018-2030 (USD MILLION)
TABLE 267. AUSTRALIA DEEP LEARNING MARKET SIZE, BY ALGORITHM TYPE, 2018-2030 (USD MILLION)
TABLE 268. AUSTRALIA DEEP LEARNING MARKET SIZE, BY HARDWARE, 2018-2030 (USD MILLION)
TABLE 269. AUSTRALIA DEEP LEARNING MARKET SIZE, BY SOFTWARE FRAMEWORKS, 2018-2030 (USD MILLION)
TABLE 270. AUSTRALIA DEEP LEARNING MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
TABLE 271. AUSTRALIA DEEP LEARNING MARKET SIZE, BY AUTONOMOUS VEHICLES, 2018-2030 (USD MILLION)
TABLE 272. AUSTRALIA DEEP LEARNING MARKET SIZE, BY IMAGE RECOGNITION, 2018-2030 (USD MILLION)
TABLE 273. AUSTRALIA DEEP LEARNING MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2018-2030 (USD MILLION)
TABLE 274. AUSTRALIA DEEP LEARNING MARKET SIZE, BY SPEECH RECOGNITION, 2018-2030 (USD MILLION)
TABLE 275. AUSTRALIA DEEP LEARNING MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
TABLE 276. AUSTRALIA DEEP LEARNING MARKET SIZE, BY FINANCE, 2018-2030 (USD MILLION)
TABLE 277. AUSTRALIA DEEP LEARNING MARKET SIZE, BY HEALTHCARE, 2018-2030 (USD MILLION)
TABLE 278. AUSTRALIA DEEP LEARNING MARKET SIZE, BY MANUFACTURING, 2018-2030 (USD MILLION)
TABLE 279. AUSTRALIA DEEP LEARNING MARKET SIZE, BY RETAIL, 2018-2030 (USD MILLION)
TABLE 280. AUSTRALIA DEEP LEARNING MARKET SIZE, BY PERFORMANCE METRICS, 2018-2030 (USD MILLION)
TABLE 281. AUSTRALIA DEEP LEARNING MARKET SIZE, BY ACCURACY, 2018-2030 (USD MILLION)
TABLE 282. AUSTRALIA DEEP LEARNING MARKET SIZE, BY COST EFFICIENCY, 2018-2030 (USD MILLION)
TABLE 283. AUSTRALIA DEEP LEARNING MARKET SIZE, BY SCALABILITY, 2018-2030 (USD MILLION)
TABLE 284. AUSTRALIA DEEP LEARNING MARKET SIZE, BY TRAINING TIME, 2018-2030 (USD MILLION)
TABLE 285. AUSTRALIA DEEP LEARNING MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
TABLE 286. AUSTRALIA DEEP LEARNING MARKET SIZE, BY CLOUD-BASED, 2018-2030 (USD MILLION)
TABLE 287. AUSTRALIA DEEP LEARNING MARKET SIZE, BY EDGE DEVICES, 2018-2030 (USD MILLION)
TABLE 288. AUSTRALIA DEEP LEARNING MARKET SIZE, BY ON-PREMISES, 2018-2030 (USD MILLION)
TABLE 289. CHINA DEEP LEARNING MARKET SIZE, BY TECHNOLOGY, 2018-2030 (USD MILLION)
TABLE 290. CHINA DEEP LEARNING MARKET SIZE, BY ALGORITHM TYPE, 2018-2030 (USD MILLION)
TABLE 291. CHINA DEEP LEARNING MARKET SIZE, BY HARDWARE, 2018-2030 (USD MILLION)
TABLE 292. CHINA DEEP LEARNING MARKET SIZE, BY SOFTWARE FRAMEWORKS, 2018-2030 (USD MILLION)
TABLE 293. CHINA DEEP LEARNING MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
TABLE 294. CHINA DEEP LEARNING MARKET SIZE, BY AUTONOMOUS VEHICLES, 2018-2030 (USD MILLION)
TABLE 295. CHINA DEEP LEARNING MARKET SIZE, BY IMAGE RECOGNITION, 2018-2030 (USD MILLION)
TABLE 296. CHINA DEEP LEARNING MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2018-2030 (USD MILLION)
TABLE 297. CHINA DEEP LEARNING MARKET SIZE, BY SPEECH RECOGNITION, 2018-2030 (USD MILLION)
TABLE 298. CHINA DEEP LEARNING MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
TABLE 299. CHINA DEEP LEARNING MARKET SIZE, BY FINANCE, 2018-2030 (USD MILLION)
TABLE 300. CHINA DEEP LEARNING MARKET SIZE, BY HEALTHCARE, 2018-2030 (USD MILLION)
TABLE 301. CHINA DEEP LEARNING MARKET SIZE, BY MANUFACTURING, 2018-2030 (USD MILLION)
TABLE 302. CHINA DEEP LEARNING MARKET SIZE, BY RETAIL, 2018-2030 (USD MILLION)
TABLE 303. CHINA DEEP LEARNING MARKET SIZE, BY PERFORMANCE METRICS, 2018-2030 (USD MILLION)
TABLE 304. CHINA DEEP LEARNING MARKET SIZE, BY ACCURACY, 2018-2030 (USD MILLION)
TABLE 305. CHINA DEEP LEARNING MARKET SIZE, BY COST EFFICIENCY, 2018-2030 (USD MILLION)
TABLE 306. CHINA DEEP LEARNING MARKET SIZE, BY SCALABILITY, 2018-2030 (USD MILLION)
TABLE 307. CHINA DEEP LEARNING MARKET SIZE, BY TRAINING TIME, 2018-2030 (USD MILLION)
TABLE 308. CHINA DEEP LEARNING MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
TABLE 309. CHINA DEEP LEARNING MARKET SIZE, BY CLOUD-BASED, 2018-2030 (USD MILLION)
TABLE 310. CHINA DEEP LEARNING MARKET SIZE, BY EDGE DEVICES, 2018-2030 (USD MILLION)
TABLE 311. CHINA DEEP LEARNING MARKET SIZE, BY ON-PREMISES, 2018-2030 (USD MILLION)
TABLE 312. INDIA DEEP LEARNING MARKET SIZE, BY TECHNOLOGY, 2018-2030 (USD MILLION)
TABLE 313. INDIA DEEP LEARNING MARKET SIZE, BY ALGORITHM TYPE, 2018-2030 (USD MILLION)
TABLE 314. INDIA DEEP LEARNING MARKET SIZE, BY HARDWARE, 2018-2030 (USD MILLION)
TABLE 315. INDIA DEEP LEARNING MARKET SIZE, BY SOFTWARE FRAMEWORKS, 2018-2030 (USD MILLION)
TABLE 316. INDIA DEEP LEARNING MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
TABLE 317. INDIA DEEP LEARNING MARKET SIZE, BY AUTONOMOUS VEHICLES, 2018-2030 (USD MILLION)
TABLE 318. INDIA DEEP LEARNING MARKET SIZE, BY IMAGE RECOGNITION, 2018-2030 (USD MILLION)
TABLE 319. INDIA DEEP LEARNING MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2018-2030 (USD MILLION)
TABLE 320. INDIA DEEP LEARNING MARKET SIZE, BY SPEECH RECOGNITION, 2018-2030 (USD MILLION)
TABLE 321. INDIA DEEP LEARNING MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
TABLE 322. INDIA DEEP LEARNING MARKET SIZE, BY FINANCE, 2018-2030 (USD MILLION)
TABLE 323. INDIA DEEP LEARNING MARKET SIZE, BY HEALTHCARE, 2018-2030 (USD MILLION)
TABLE 324. INDIA DEEP LEARNING MARKET SIZE, BY MANUFACTURING, 2018-2030 (USD MILLION)
TABLE 325. INDIA DEEP LEARNING MARKET SIZE, BY RETAIL, 2018-2030 (USD MILLION)
TABLE 326. INDIA DEEP LEARNING MARKET SIZE, BY PERFORMANCE METRICS, 2018-2030 (USD MILLION)
TABLE 327. INDIA DEEP LEARNING MARKET SIZE, BY ACCURACY, 2018-2030 (USD MILLION)
TABLE 328. INDIA DEEP LEARNING MARKET SIZE, BY COST EFFICIENCY, 2018-2030 (USD MILLION)
TABLE 329. INDIA DEEP LEARNING MARKET SIZE, BY SCALABILITY, 2018-2030 (USD MILLION)
TABLE 330. INDIA DEEP LEARNING MARKET SIZE, BY TRAINING TIME, 2018-2030 (USD MILLION)
TABLE 331. INDIA DEEP LEARNING MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
TABLE 332. INDIA DEEP LEARNING MARKET SIZE, BY CLOUD-BASED, 2018-2030 (USD MILLION)
TABLE 333. INDIA DEEP LEARNING MARKET SIZE, BY EDGE DEVICES, 2018-2030 (USD MILLION)
TABLE 334. INDIA DEEP LEARNING MARKET SIZE, BY ON-PREMISES, 2018-2030 (USD MILLION)
TABLE 335. INDONESIA DEEP LEARNING MARKET SIZE, BY TECHNOLOGY, 2018-2030 (USD MILLION)
TABLE 336. INDONESIA DEEP LEARNING MARKET SIZE, BY ALGORITHM TYPE, 2018-2030 (USD MILLION)
TABLE 337. INDONESIA DEEP LEARNING MARKET SIZE, BY HARDWARE, 2018-2030 (USD MILLION)
TABLE 338. INDONESIA DEEP LEARNING MARKET SIZE, BY SOFTWARE FRAMEWORKS, 2018-2030 (USD MILLION)
TABLE 339. INDONESIA DEEP LEARNING MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
TABLE 340. INDONESIA DEEP LEARNING MARKET SIZE, BY AUTONOMOUS VEHICLES, 2018-2030 (USD MILLION)
TABLE 341. INDONESIA DEEP LEARNING MARKET SIZE, BY IMAGE RECOGNITION, 2018-2030 (USD MILLION)
TABLE 342. INDONESIA DEEP LEARNING MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2018-2030 (USD MILLION)
TABLE 343. INDONESIA DEEP LEARNING MARKET SIZE, BY SPEECH RECOGNITION, 2018-2030 (USD MILLION)
TABLE 344. INDONESIA DEEP LEARNING MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
TABLE 345. INDONESIA DEEP LEARNING MARKET SIZE, BY FINANCE, 2018-2030 (USD MILLION)
TABLE 346. INDONESIA DEEP LEARNING MARKET SIZE, BY HEALTHCARE, 2018-2030 (USD MILLION)
TABLE 347. INDONESIA DEEP LEARNING MARKET SIZE, BY MANUFACTURING, 2018-2030 (USD MILLION)
TABLE 348. INDONESIA DEEP LEARNING MARKET SIZE, BY RETAIL, 2018-2030 (USD MILLION)
TABLE 349. INDONESIA DEEP LEARNING MARKET SIZE, BY PERFORMANCE METRICS, 2018-2030 (USD MILLION)
TABLE 350. INDONESIA DEEP LEARNING MARKET SIZE, BY ACCURACY, 2018-2030 (USD MILLION)
TABLE 351. INDONESIA DEEP LEARNING MARKET SIZE, BY COST EFFICIENCY, 2018-2030 (USD MILLION)
TABLE 352. INDONESIA DEEP LEARNING MARKET SIZE, BY SCALABILITY, 2018-2030 (USD MILLION)
TABLE 353. INDONESIA DEEP LEARNING MARKET SIZE, BY TRAINING TIME, 2018-2030 (USD MILLION)
TABLE 354. INDONESIA DEEP LEARNING MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
TABLE 355. INDONESIA DEEP LEARNING MARKET SIZE, BY CLOUD-BASED, 2018-2030 (USD MILLION)
TABLE 356. INDONESIA DEEP LEARNING MARKET SIZE, BY EDGE DEVICES, 2018-2030 (USD MILLION)
TABLE 357. INDONESIA DEEP LEARNING MARKET SIZE, BY ON-PREMISES, 2018-2030 (USD MILLION)
TABLE 358. JAPAN DEEP LEARNING MARKET SIZE, BY TECHNOLOGY, 2018-2030 (USD MILLION)
TABLE 359. JAPAN DEEP LEARNING MARKET SIZE, BY ALGORITHM TYPE, 2018-2030 (USD MILLION)
TABLE 360. JAPAN DEEP LEARNING MARKET SIZE, BY HARDWARE, 2018-2030 (USD MILLION)
TABLE 361. JAPAN DEEP LEARNING MARKET SIZE, BY SOFTWARE FRAMEWORKS, 2018-2030 (USD MILLION)
TABLE 362. JAPAN DEEP LEARNING MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
TABLE 363. JAPAN DEEP LEARNING MARKET SIZE, BY AUTONOMOUS VEHICLES, 2018-2030 (USD MILLION)
TABLE

Companies Mentioned

  • Advanced Micro Devices, Inc.
  • ARM Ltd.
  • Broadcom Corporation
  • CEVA Inc.
  • Clarifai, Inc.
  • Google LLC
  • Huawei Technologies
  • Intel Corporation
  • International Business Machines Corporation
  • Microsoft Corporation
  • Neurala
  • NVIDIA Corporation
  • OpenAI
  • Qualcomm Technologies, Inc
  • Samsung Group
  • Starmind

Methodology

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