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1027477

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

Published: | Frost & Sullivan | 55 Pages | Delivery time: 1-2 business days

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Next Wave of Deep Learning Models & Applications (RNN, CNN, and GaN)
Published: August 18, 2021
Frost & Sullivan
Content info: 55 Pages
Delivery time: 1-2 business days
  • Description
  • Table of Contents

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)
Product Code: DA02

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