Huawei technologies co., ltd. (20240095531). METHOD AND COMPUTER SYSTEM FOR TRAINING A NEURAL NETWORK MODEL simplified abstract
Contents
- 1 METHOD AND COMPUTER SYSTEM FOR TRAINING A NEURAL NETWORK MODEL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND COMPUTER SYSTEM FOR TRAINING A NEURAL NETWORK MODEL - 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 AND COMPUTER SYSTEM FOR TRAINING A NEURAL NETWORK MODEL
Organization Name
Inventor(s)
METHOD AND COMPUTER SYSTEM FOR TRAINING A NEURAL NETWORK MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095531 titled 'METHOD AND COMPUTER SYSTEM FOR TRAINING A NEURAL NETWORK MODEL
Simplified Explanation
A method trains a neural network model in a computer system. The neural network model consists of one or more layers, each containing one or more neurons. The layers include at least one first layer and one last layer. Neurons in each layer perform forward propagation of input values by applying weights and generating an output value based on a function applied to the sum of the weighted input values. Neurons in each layer, except the last layer, are connected with neurons in the consecutive layer. Neurons in each layer, except the first layer, are connected with neurons in the preceding layer.
- Neural network model training method in a computer system
- Layers with neurons performing forward propagation of input values
- Connections between neurons in consecutive and preceding layers
Potential Applications
This technology can be applied in various fields such as:
- Image recognition
- Speech recognition
- Natural language processing
Problems Solved
This technology helps in:
- Improving accuracy in pattern recognition tasks
- Enhancing decision-making processes
- Automating complex data analysis tasks
Benefits
The benefits of this technology include:
- Faster processing of large datasets
- Enhanced predictive capabilities
- Improved efficiency in data-driven tasks
Potential Commercial Applications
This technology can be utilized in industries such as:
- Healthcare for medical image analysis
- Finance for fraud detection
- Marketing for customer behavior analysis
Possible Prior Art
Prior art in neural network training methods includes:
- Backpropagation algorithm
- Convolutional neural networks
Unanswered Questions
How does this technology compare to existing neural network training methods?
This article does not provide a direct comparison with other neural network training methods. Further research is needed to understand the specific advantages and limitations of this approach compared to existing techniques.
What are the computational requirements for implementing this neural network model training method?
The article does not detail the computational resources needed for implementing this method. Understanding the computational demands can help in assessing the feasibility of deploying this technology in real-world applications.
Original Abstract Submitted
a method trains a neural network model in a computer system. the neural network model includes one or more layers each including one or more neurons. the one or more layers include at least one first layer and one last layer. each neurons are configured to perform forward propagation of one or more input values by applying weights to the one or more input values and generating an output value based on a function applied to the sum of the weighted input values. the neurons of any given layer, but the last layer, of the one or more layers are connected with the one or more neurons of a consecutive layer. the neurons of any given layer, but the first layer, of the one or more layers are connected with the one or more neurons of a preceding layer.
