Global Deep Learning Market Research Report Forecast to 2023

Global Deep Learning Market Research Report: by Component (Hardware, Software, Services), Application (Image Recognition, Data Mining, Signal Recognition), End User (Security, Manufacturing, BFSI, Healthcare, Agriculture) and Region - Forecast till 2023
Market analysis

Global deep learning market is relied upon to observe significant development during the conjecture time frame. North America is assessed to be a prominent area for deep learning market because of the nearness of key market players, vigorously putting resources into the innovative work of profound learning programming, stages, applications, and frameworks over the US, Canada, and Mexico. So also, expanding interest for deep learning innovation for regular language handling and voice/discourse acknowledgment in the European budgetary administration industry is anticipated to drive the profound learning market in the coming years. In November 2017, Google built up its coordinated effort apparatus named Colaboratory, that can run code and show yield inside the report. It depends on Jupyter (an open-source stage for programming advancement utilizing python) and enables the clients to share and utilize note pads with another client without introducing it on the PC. the market is expected to grow at 30.87% CAGR during the forecast period.

Market segmentation

The global deep learning market is segmented on the basis of its component, end-user, application, and regional demand. On the basis of its component, the market is bifurcated into Software, Hardware, and Services. Based on its application, the market is bifurcated into Signal Recognition, Data Mining, Image Recognition, and Others. On the basis of its End-User, the market is segmented into Security, Retail, Manufacturing, Automotive, BFSI, Media & Entertainment, Agriculture, Healthcare, among others.

Regional analysis

Geographically, the global deep learning market is divided into global regions like Europe, North America, Asia- Pacific, Middle East, LATAM, and Africa.

Major players

Mellanox Technologies (USA), Adapteva, Inc. (USA), Qualcomm Technologies Inc. (USA), NVIDIA Corporation (USA), Baidu Inc (China), IBM Corporation (USA), Amazon Inc. (USA), Intel Corporation (USA), Samsung Electronics Co Ltd (South Korea), Micron Technology (USA), Sensory Inc. (USA), Xilinx Inc. (USA), Google LLC (USA), among others are some of the major players in the global deep learning market.

Table of Contents:

1 Executive Summary
2 Market Introduction
2.1 Definition
2.2 Scope of the Study
2.3 Market Structure
3 Research Methodology
3.1 Research Process
3.2 List of Assumptions
3.3 Forecast Model
4 Market Dynamics
4.1 Introduction
4.2 Drivers
4.2.1 Increasing adoption of cloud-based services
4.2.2 Increasing unstructured data leads to the increasing demand for deep learning solutions
4.3 Restraint
4.3.1 Lack of technical expertise
4.4 Opportunities
4.4.1 Advantages of deep learning solutions in healthcare, agriculture, and marketing automation application
4.5 Challenge
4.5.1 Requirement of massive datasets for training
4.6 Value Chain Analysis
4.6.1 Design
4.6.2 Card/Processors
4.6.3 System & Software
4.6.4 End–users & Services
4.7 Porter’s Five Forces Model
4.7.1 Threat of New Entrants
4.7.2 Bargaining Power of Suppliers
4.7.3 Bargaining Power of Buyers
4.7.4 Threat of Substitutes
4.7.5 Intensity of Rivalry
4.8 Evolution of Machine Learning
4.8.1 Correlation of Machine Learning and Deep Learning
4.8.2 Machine Learning Adoption by Region
4.8.2.1 North America
4.8.2.2 Europe
4.8.2.3 Asia-Pacific
4.8.2.4 Rest of the world (ROW)
4.9 Evolution of Deep Learning
4.1 Deep Learning Life Cycle
4.10.1 Defining Project Objectives
4.10.2 Acquiring and Exploring Data
4.10.3 Data Modelling
4.10.4 Interpretation and Communication
4.10.5 Implementation, Documentation and Maintenance
4.11 Use Cases
4.11.1 Drug Discovery and Medical Treatment
4.11.2 Predictive Maintenance
4.11.3 Customer Service Management and Personalized Service Offerings
4.11.4 Logistics Optimization
4.11.5 Voice and Image Recognition
4.12 Deep Learning Architectures/ Models
4.12.1 Recurrent Neural Network (RNN)
4.12.2 Convolutional Neural Network (CNN)
4.12.3 Unsupervised Pretrained Network (UPN)
4.12.3.1 Deep Belief Network (DBN)
4.12.3.2 Generative Adversarial Network (GAN)
5 Global Deep Learning Market, By Component
5.1 Overview
5.1.1 Hardware
5.1.1.1 Processors
5.1.1.2 Memory
5.1.1.3 Network
5.1.2 Software
5.1.2.1 Solution
5.1.2.2 Platform
5.1.3 Services
5.1.3.1 Installation
5.1.3.2 Training
5.1.3.3 Support & Maintenance
6 Global Deep Learning Market, By Application
6.1 Introduction
6.1.1 Image Recognition
6.1.2 Data Mining
6.1.3 Signal Recognition
6.1.4 Others
7 Global Deep Learning Market, By End-User
7.1 Introduction
7.1.1 Security
7.1.2 Manufacturing
7.1.3 Retail
7.1.4 Automotive
7.1.5 Media & Entertainment
7.1.6 BFSI
7.1.7 Healthcare
7.1.8 Agriculture
7.1.9 Others
8 Global Deep Learning Market, By Region
8.1 Introduction
8.2 North America
8.2.1 US
8.2.2 Canada
8.2.3 Mexico
8.3 Europe
8.3.1 UK
8.3.2 Germany
8.3.3 Spain
8.3.4 France
8.3.5 Rest of Europe
8.4 Asia-Pacific
8.4.1 China
8.4.2 Japan
8.4.3 Australia
8.4.4 India
8.4.5 Rest of Asia-Pacific
8.5 Rest of the World (ROW)
8.5.1 Middle East & Africa
8.5.2 South America
9 Competitive Landscape
9.1 Key Players Market Share Analysis, 2018 (%)
10 Company Profiles
10.1 Amazon Inc.
10.1.1 Company Overviews
10.1.2 Financial Overview
10.1.4 Key Developments
10.1.5 SWOT Analysis
10.1.6 Key Strategies
10.2 Intel Corporation
10.2.1 Company Overviews
10.2.2 Financial Overview
10.2.3 Products/Services/Solutions Offered
10.2.4 Key Developments
10.2.5 SWOT Analysis
10.2.6 Key Strategies
10.3 Samsung Electronics Co Ltd
10.3.1 Company Overviews
10.3.2 Financial Overview
10.3.4 Key Developments
10.3.5 SWOT Analysis
10.3.6 Key Strategies
10.4 Micron Technology
10.4.1 Company Overviews
10.4.2 Financial Overview
10.4.4 Key Developments
10.4.5 SWOT Analysis
10.4.6 Key Strategies
10.5 Sensory Inc
10.5.1 Company Overviews
10.5.3 Key Developments
10.5.4 SWOT Analysis
10.5.5 Key Strategies
10.6 Xilinx Inc
10.6.1 Company Overviews
10.6.2 Financial Overview
10.6.3 Products/Services/Solutions Offered
10.6.4 Key Developments
10.6.5 SWOT Analysis
10.6.6 Key Strategies
10.7 Google LLC
10.7.1 Company Overviews
10.7.2 Financial Overview
10.7.4 Key Developments
10.7.5 SWOT Analysis
10.7.6 Key Strategies
10.8 Mellanox Technologies
10.8.1 Company Overviews
10.8.2 Financial Overview
10.8.4 Key Developments
10.8.5 SWOT Analysis
10.8.6 Key Strategies
10.9 Adapteva, Inc.
10.9.1 Company Overviews
10.9.3 SWOT Analysis
10.1 Qualcomm Technologies Inc
10.10.1 Company Overviews
10.10.2 Financial Overview
10.10.4 Key Developments
10.10.5 SWOT Analysis
10.10.6 Key Strategies
10.11 NVIDIA Corporation
10.11.1 Company Overviews
10.11.2 Financial Overview
10.11.3 Products/Services/Solutions Offered
10.11.4 Key Developments
10.11.5 SWOT Analysis
10.11.6 Key Strategies
10.12 Baidu Inc
10.12.1 Company Overviews
10.12.2 Financial Overview
10.12.4 Key Developments
10.12.5 SWOT Analysis
10.12.6 Key Strategies
10.13 IBM Corporation
10.13.1 Company Overviews
10.13.2 Financial Overview
10.13.4 Key Developments
10.13.5 SWOT Analysis
10.13.6 Key Strategies
10.14 Advanced Micro Devices Inc
10.14.1 Company Overviews
10.14.2 Financial Overview
10.14.4 Key Developments
10.14.5 SWOT Analysis
10.14.6 Key Strategies
10.15 Microsoft Corporation
10.15.1 Company Overview
10.15.2 Financial Overview
10.15.3 Products/Services Offered
10.15.4 Key Developments
10.15.5 SWOT Analysis
10.15.6 Key Strategies
10.16 Facebook
10.16.1 Company Overview
10.16.2 Financial Overview
10.16.3 Products/Services Offered
10.16.4 Key Developments
10.16.5 SWOT Analysis
10.16.6 Key Strategies
10.17 Tenstorrent
10.17.1 Company Overview
10.17.2 Products/Services/Solutions Offered