Global Deep Learning Market (2019-2027)

  • Report Code : 251095
  • Industry : Telecom and IT
  • Published On : Jul 2018
  • Pages : 100
  • Publisher : Netscribes
  • Format: CMI Insight PPT FormatCMI Insight PDF Format

Impact Analysis of Covid-19

The complete version of the Report will include the impact of the COVID-19, and anticipated change on the future outlook of the industry, by taking into the account the political, economic, social, and technological parameters.

Global Deep Learning Market

In enterprise computing, deep learning is evolving into one of the most advanced technologies. Deep learning is a subset of machine learning in Artificial Intelligence (AI) that has networks capable of learning unsupervised, from data that is unstructured or unlabeled.
By offering expert assistance, it would be able to assist humans in extending their capabilities. Organizations are using deep learning networks to get valuable insights from huge amount of data. This is done to provide innovative products and better improved customer experiences, thereby raising revenue opportunities for the market. The global deep learning market is anticipated to reach USD 28.83 Bn and expand at a CAGR of 48.4% during the forecast period of 2018-2023.

Deep learning techniques are used to develop new technologies such as natural language processing and visual data mining, to enhance product offerings. The growing need for deep learning in database systems, fraud detection and cyber security, is driving the growth process of data mining applications in the deep learning market. The market is classified into three primary segments - based on solution, application and end user.
Based on solution: Hardware, software and services
Based on application: Image recognition, signal recognition, data mining, and others
Based on end user: Healthcare, BFSI, aerospace and defense, automotive, retail and media and entertainment and others (manufacturing, oil, gas and energy)
On the basis of regions, the market is segmented into North America, Europe, Asia-Pacific, Latin America, and the Middle East and Africa.

Key growth factors

Deep learning offers faster and better memory utilization in comparison to traditional computing systems. Rising usage of deep learning technology among various industries such as automotive, advertisement, medical fuel the growth of the market. Robust research and development for the expansion of better processing hardware for deep learning, growing necessity for hardware platforms with high computing power to execute deep learning algorithms are key driving factors of deep learning market. Increasing acceptance of cloud based technology, high usage of deep learning in big data analytics, and rising applicability in healthcare and autonomous vehicles are accelerating growth.

Threats and key players

Deep learning requires high-performance hardware, which is not easily available. Greater complexities in hardware owing to complex algorithm in deep learning technology, can hamper the growth of the market. Many organizations prefer the traditional route over hyper parameter optimization, thereby restricting the revenue growth of the deep learning market.
Some of the prominent competitors in deep learning market are Google Inc., Microsoft Corporation, Qualcomm Technologies, Inc., IBM Corporation, Intel Corporation, General Vision Inc. and NVIDIA Corporation, etc.

What's covered in the report?

1. Overview of the global deep learning market.
2. Market drivers and challenges of the global deep learning market.
3. Market trends in global deep learning market.
4. Historical, current and forecasted market size data for the segment based on solution.
5. Historical, current and forecasted market size data for the segment based on application.
6. Historical, current and forecasted market size data for the segment based on end user.
7. Historical, current and forecasted regional (North America, Europe, Asia-Pacific, Latin America, the Middle East & Africa) market size data for the deep learning market.
8. Historical, current and forecasted market size data for region-wise segments.
9. Analysis of company profiles of major competitors operating in the market.

Why buy?

1. Understand the demand for deep learning to determine the viability of the market
2. Determine the developed and emerging markets of deep learning
3. Identify the challenge areas and address them
4. Develop strategies based on the drivers, trends and highlights for each of the segments
5. Evaluate the value chain to determine the workflow and to get an idea of the current position where you are placed
6. Recognize the key competitors of this market and respond accordingly
7. Knowledge of the initiatives and growth strategies taken by the major companies and decide the direction of further growth
8. Define the competitive positioning by comparing the products and services with the key players in the market
9. Recognize the extent and nature of the start-ups providing deep learning solutions

Customizations available

With the given market data, Netscribes offers customizations according to specific needs. Write to us at [email protected]

Chapter 1: Executive summary

1.1. Market scope and segmentation
1.2. Key questions answered in this study
1.3. Executive summary

Chapter 2: Global deep learning market - market overview

2.1. Market definitions
2.2. Global market overview- market trends, market attractiveness analysis, geography wise market revenue (USD)
2.3. Global - market drivers and challenges
2.4. Value chain analysis - Global deep learning market
2.5. Porter's Five Forces Analysis
2.6. Market size- By solution (hardware revenue, software revenue and services revenue)
2.6. a. Hardware- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.6. b. Software- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.6. c. Services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.7. Market size- By application (Image recognition, signal recognition, data mining, and others)
2.7. a. Image recognition - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.7. b. Signal recognition - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.7. c. Data mining - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.7. d. Others - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.8. Market size- By end user (Healthcare, BFSI, aerospace and defense, automotive, retail, media and entertainment, others )
2.8. a. Healthcare - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.8. b. BFSI - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.8. c. Automotive - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.8. d. Retail - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.8. e. Media and entertainment - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
2.8. f. Others (manufacturing, oil, gas and energy) - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations

Chapter 3: North America deep learning market- market overview

3.1. Market overview- market trends, market attractiveness analysis, geography wise market revenue (USD)
3.2. North America - market drivers and challenges
3.3. Market size- By solution (hardware revenue, software revenue and services revenue)
3.3. a. Hardware- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.3. b. Software- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.3. c. Services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.4. Market size- By application (Image recognition, signal recognition, data mining, and others)
3.4. a. Image recognition - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.4. b. Signal recognition - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.4. c. Data mining - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.4. d. Others - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.5. Market size- By end user (Healthcare, BFSI, aerospace and defense, automotive, retail, media and entertainment, others)
3.5. a. Healthcare - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.5. b. BFSI - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.5. c. Automotive - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.5. d. Retail - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.5. e. Media and entertainment - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
3.5. f. Others (manufacturing, oil, gas and energy) - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations

Chapter 4: Europe deep learning market- market overview

4.1. Market overview- market trends, market attractiveness analysis, geography wise market revenue (USD)
4.2. Europe - market drivers and challenges
4.3. Market size- By solution (hardware revenue, software revenue and services revenue)
4.3. a. Hardware- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.3. b. Software- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.3. c. Services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.4. Market size- By application (Image recognition, signal recognition, data mining, and others)
4.4. a. Image recognition - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.4. b. Signal recognition - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.4. c. Data mining - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.4. d. Others - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.5. Market size- By end user (Healthcare, BFSI, aerospace and defense, automotive, retail, media and entertainment, others)
4.5. a. Healthcare - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.5. b. BFSI - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.5. c. Automotive - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.5. d. Retail - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.5. e. Media and entertainment - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
4.5. f. Others (manufacturing, oil, gas and energy)- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations

Chapter 5: Asia-Pacific deep learning market- market overview

5.1. Market overview- market trends, market attractiveness analysis, geography wise market revenue (USD)
5.2. Asia Pacific- market drivers and challenges
5.3. Market size- By solution (hardware revenue, software revenue and services revenue)
5.3. a. Hardware- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.3. b. Software- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.3. c. Services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.4. Market size- By application (Image recognition, signal recognition, data mining, and others)
5.4. a. Image recognition - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.4. b. Signal recognition - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.4. c. Data mining - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.4. d. Others - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.5. Market size- By end user (Healthcare, BFSI, aerospace and defense, automotive, retail, media and entertainment, others)
5.5. a. Healthcare - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.5. b. BFSI - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.5. c. Automotive - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.5. d. Retail - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.5. e. Media and entertainment - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
5.5. f. Others (manufacturing, oil, gas and energy)- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations

Chapter 6: Latin America deep learning market- market overview

6.1. Market overview- market trends, market attractiveness analysis, geography wise market revenue (USD)
6.2. Latin America- market drivers and challenges
6.3. Market size- By solution (hardware revenue, software revenue and services revenue)
6.3. a. Hardware- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.3. b. Software- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.3. c. Services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.4. Market size- By application (Image recognition, signal recognition, data mining, and others)
6.4. a. Image recognition - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.4. b. Signal recognition - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.4. c. Data mining - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.4. d. Others - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.5. Market size- By end user (Healthcare, BFSI, aerospace and defense, automotive, retail, media and entertainment, others)
6.5. a. Healthcare - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.5. b. BFSI - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.5. c. Automotive - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.5. d. Retail - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.5. e. Media and entertainment - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
6.5. f. Others (manufacturing, oil, gas and energy)- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations

Chapter 7: Middle East and Africa deep learning market- market overview

7.1. Market overview- market trends, market attractiveness analysis, geography wise market revenue (USD)
7.2. Middle East and Africa- market drivers and challenges
7.3. Market size- By solution (hardware revenue, software revenue and services revenue)
7.3. a. Hardware- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.3. b. Software- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.3. c. Services- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.4. Market size- By application (Image recognition, signal recognition, data mining, and others)
7.4. a. Image recognition - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.4. b. Signal recognition - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.4. c. Data mining - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.4. d. Others - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.5. Market size- By end user (Healthcare, BFSI, aerospace and defense, automotive, retail, media and entertainment, others)
7.5. a. Healthcare - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.5. b. BFSI - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.5. c. Automotive - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.5. d. Retail - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.5. e. Media and entertainment - Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations
7.5. f. Others (manufacturing, oil, gas and energy)- Historical (2015-2017) and forecasted (2018-2023) market size (USD Bn), key observations

Chapter 8: Competitive landscape

8.1. Google Inc.
8.1. a. Company snapshot
8.1. b. Product offerings
8.1. c. Growth strategies
8.1. d. Initiatives
8.1. e. Geographical presence
8.1. f. Key numbers
8.2. Microsoft Corporation
8.3. Qualcomm Technologies
8.4. IBM Corporation
8.5. Intel Corporation
8.6. General Vision Inc.
8.7. NVIDIA Corporation

Chapter 9: Conclusion

Chapter 10: Appendix

10.1. List of tables
10.2. Research methodology
10.3. Assumptions
10.4. About Netscribes Inc.

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