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市場調查報告書
商品編碼
1027477

深度學習模型和應用展望(RNN、CNN、GaN)

Next Wave of Deep Learning Models & Applications (RNN, CNN, and GaN)

出版日期: | 出版商: Frost & Sullivan | 英文 55 Pages | 商品交期: 最快1-2個工作天內

價格
  • 全貌
  • 簡介
  • 目錄
簡介

隨著數字化進程的推進,業務流程變得更加自動化,人工智能正被引入無處不在。與此同時,對於可以使用 AI 實現什麼樣的應用程序,人們對 AI 的期望也越來越高。因此,正在引入更複雜的神經網絡,並有望利用計算能力的進步來實現人工智能將具有更大決策權和自主權的下一代應用程序。

學術界和工業界之間的出色合作正在加速圍繞 AI 和 ML 的新研究項目的商業化。谷歌和英偉達等公司也在人工智能應用研究方面處於領先地位,推動了自動駕駛汽車、仿真軟件和其他智能應用基礎算法的開發。

本報告調查了深度學習模型和應用,包括 AI 範圍、深度學習和神經網絡概述、卷積神經網絡 (CNN)、循環神經網絡 (RNN) 和生成對抗網絡(它提供了 GAN 等信息),增長機會和主要公司的簡介。

目錄

第 1 章戰略要求

  • 增長難度:對增長造成壓力的戰略要務 8 (TM) 因素
  • 戰略要務 8 (TM)
  • 人工智能產業的三大戰略勢在必行
  • 關於增長管道引擎 (TM)
  • 增長機會對增長管道引擎 (TM) 的影響

第二章調查範圍和調查方法

  • 調查範圍
  • 調查方法
  • 調查方法概要

第三章介紹-人工智能和神經網絡

  • AI 系統已經發展到滿足需要更高自主性的現代應用程序的期望。
  • 神經網絡應用於各個工業領域,並受益於高性能計算機的廣泛使用。
  • 監督學習是關鍵商業 AI 應用的基礎,但其他框架也具有廣闊的潛力。
  • 使用神經網絡進行深度學習可實現複雜的分層決策
  • 神經網絡採用複雜的逐步決策過程來模擬人類決策。

第4章卷積神經網絡(CNN)

  • CNN 使用卷積和池化層來處理圖像
  • CNN擅長簡化複雜輸入數據的特徵,加快處理速度
  • CNN 在多個商業應用中已成為計算機視覺的核心
  • CNN 用於檢測圖像中的細微障礙物和異常,加速故障檢測過程。

第5章循環神經網絡(RNN)

  • RNN 適用於需要順序數據處理的應用
  • RNN 具有內部存儲器,可以在前一個輸入的上下文中處理輸入。
  • Google、Siri 和 Alexa 等語音助手使用 RNN 語音分析和上下文分析。
  • 當前的 RNN 應用僅限於語音和語音,但圖像分析和機器人等新應用也在不斷湧現。

第6章生成對抗網絡(GAN)

  • GAN 利用神經網絡零和遊戲推導出輸入數據的真實副本
  • GAN 是 UL 方法的增強版本,可自動執行持續學習過程。
  • GAN 非常適合需要自動化創造性決策過程的應用
  • CNN 用作分類器和生成器,可在醫療保健和娛樂領域實現各種應用。

第 7 章主要公司

  • Google
  • Nvidia
  • Adobe
  • Microsoft
  • IBM

第 8 章增長機會

  • 增長機會 1:傳統保守行業的數據貨幣化和數據中介
  • 增長機會 2:AI 框架測試平台和模擬環境
  • 增長機會 3:將神經網絡集成到商業應用中

第9章主要聯繫人

第 10 章後續步驟

目錄
Product Code: DA02

Neural Networks Empowering the Next Generation of AI Applications

As digitization progresses across industries, AI is gaining a ubiquitous adoption as more and more business processes are being automated. With this, the expectations from AI in terms of what applications can be realized using AI is also expanding, and thus a more complex set of neural networks have been introduced which are expected to leverage advancements in computing power to empower the next generation of applications where AI will have a higher decision making power and autonomy over decision making.

An impressive collaboration between academia and industry has accelerated the commercialization of novel research projects surrounding AI and ML. Companies such as Google and Nvidia have also taken a lead in applied research around AI which has translated into development of algorithms which now form the base of autonomous cars , simulation software and other intelligent applications.

In brief, this research study highlights the following points:

  • Scope of AI, Deep Learning and Neural Network
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Generative Adversarial Networks (GAN)

Table of Contents

1.0 Strategic Imperatives

  • 1.1. Why Is It Increasingly Difficult to Grow? The Strategic Imperative 8™: Factors Creating Pressure on Growth
  • 1.2. The Strategic Imperative 8™
  • 1.3. The Impact of the Top 3 Strategic Imperatives on the Artificial Intelligence Industry
  • 1.4. About the Growth Pipeline Engine™
  • 1.5. Growth Opportunities Fuel the Growth Pipeline Engine™

2.0 Research Scope and Methodology

  • 2.1. Research Scope
  • 2.2. Research Methodology
  • 2.3. Research Methodology Explained

3.0 Introduction-AI and Neural Networks

  • 3.1. AI Systems Have Evolved to Address the Expectations of Modern Applications, Which Demand Higher Levels of Autonomy
  • 3.2. Neural Networks Have Found Applications Across Industries and Have Benefitted From the Ubiquity of High-performance Computing
  • 3.3. While Supervised Learning Supports Most Major Commercial AI Applications, Other Frameworks are Showing Promising Potential
  • 3.4. Deep Learning Supported by Neural Networks has Enabled Complex and Layered Decision Making
  • 3.5. Neural Networks Employ a Complex Stepwise Decision-making Process That Emulates Human Decision Making

4.0 Convolutional Neural Networks

  • 4.1. CNNs Rely on a Series on Convolution and Pooling Layers to Process Images
  • 4.2. CNNs Excel in Simplifying Complex Input Data Characteristics for Faster Processing
  • 4.3. CNNs are the Heart of Computer Vision in Several Commercial Applications
  • 4.4. CNNs Have Been Used to Spot Microscopic Faults and Anomalies in Images, Which Accelerates Fault Detection Processes

5.0 Recurrent Neural Networks

  • 5.1. RNNs are Suited for Applications That Need Sequential Data Processing
  • 5.2. RNNs Have Internal Memory That Allows Them to Process Inputs in Context of Previous Inputs
  • 5.3. Voice Assistants such as Google, Siri, and Alexa Depend on RNNs for Speech and Context Analysis
  • 5.4. While Current Applications of RNNs Cater to Voice and Speech, Novel Applications in Image Analytics and Robotics are Emerging

6.0 Generative Adversarial Networks

  • 6.1. GANs Make use of Neural Networks in a Zero-sum Game to Derive a Realistic Replica of Input Data
  • 6.2. GANs are an Enhancement to the UL Approach That Automates the Continuous Learning Process
  • 6.3. GANs are Highly Suited to Applications Where the Process of Creative Decision Making Needs to be Automated
  • 6.4. CNNs Used as Discriminators and Generators are Enabling a Range of Applications in Healthcare and Entertainment

7.0 Companies to Action

  • 7.1. Google
  • 7.2. Nvidia
  • 7.3. Adobe
  • 7.4. Microsoft
  • 7.5. IBM

8.0 Growth opportunities

  • 8.1. Growth opportunity 1: Data Monetization And Data Brokering for Traditionally Conservative Industries
  • 8.1. Growth Opportunity 1: Data Monetization and Brokering for Traditionally Conservative Industries (continued)
  • 8.2. Growth opportunity 2: Test Beds and Simulated Environments for AI Frameworks
  • 8.2. Growth Opportunity 2: Test Beds and Simulated Environments for AI Frameworks (continued)
  • 8.3. Growth opportunity 3: Out of Box Integrations of Neural Networks with Commercial Applications
  • 8.3. Growth Opportunity 3: Out-of-Box Integration of Neural Networks With Commercial Applications (continued)

9.0 Key Contacts

  • 9.1. Key Contacts

10.0 Next Steps

  • 10.1. Your Next Steps
  • 10.2. Why Frost, Why Now?
  • Legal Disclaimer