Press release
Global Deep Learning Market Witnessing Robust Growth and Growing at the CAGR of 48.4% during forecast period
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.
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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.
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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. Porters 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
.....
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