18383254. METHOD OF LEARNING NEURAL NETWORK, RECORDING MEDIUM, AND REMAINING LIFE PREDICTION SYSTEM simplified abstract (NEC Corporation)
Contents
- 1 METHOD OF LEARNING NEURAL NETWORK, RECORDING MEDIUM, AND REMAINING LIFE PREDICTION SYSTEM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD OF LEARNING NEURAL NETWORK, RECORDING MEDIUM, AND REMAINING LIFE PREDICTION SYSTEM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
METHOD OF LEARNING NEURAL NETWORK, RECORDING MEDIUM, AND REMAINING LIFE PREDICTION SYSTEM
Organization Name
Inventor(s)
Masanao Natsumeda of Tokyo (JP)
METHOD OF LEARNING NEURAL NETWORK, RECORDING MEDIUM, AND REMAINING LIFE PREDICTION SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18383254 titled 'METHOD OF LEARNING NEURAL NETWORK, RECORDING MEDIUM, AND REMAINING LIFE PREDICTION SYSTEM
Simplified Explanation
The abstract of the patent application describes a method of learning a neural network.
- The method involves providing a simplified explanation of the neural network.
- The method includes training the neural network using specific techniques.
- The method may involve adjusting the parameters of the neural network based on feedback.
- The method aims to improve the performance and accuracy of the neural network.
Potential Applications
This technology could be applied in various fields such as:
- Image recognition
- Natural language processing
- Autonomous vehicles
- Medical diagnosis
Problems Solved
This technology helps in addressing the following issues:
- Improving the accuracy of neural networks
- Enhancing the performance of machine learning models
- Streamlining the training process for neural networks
Benefits
The benefits of this technology include:
- Increased efficiency in learning neural networks
- Enhanced accuracy in predictions
- Faster training times for machine learning models
Potential Commercial Applications
The potential commercial applications of this technology include:
- Developing advanced AI systems
- Creating innovative products in various industries
- Offering consulting services for machine learning implementation
Possible Prior Art
One possible prior art for this technology could be the use of gradient descent algorithms in training neural networks.
Unanswered Questions
How does this method compare to existing techniques for training neural networks?
This method could be compared to other methods such as backpropagation and reinforcement learning to understand its effectiveness and efficiency in training neural networks.
What impact could this method have on the field of artificial intelligence in the long term?
Exploring the long-term implications of this method on the advancement of AI technologies and applications could provide insights into its potential future developments and innovations.
Original Abstract Submitted
A method of learning a neural network includes:
