18521763. METHOD AND COMPUTER SYSTEM FOR TRAINING A NEURAL NETWORK MODEL simplified abstract (Huawei Technologies Co., Ltd.)
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 18521763 titled 'METHOD AND COMPUTER SYSTEM FOR TRAINING A NEURAL NETWORK MODEL
Simplified Explanation
The abstract describes a method for training a neural network model in a computer system, where the model consists of layers of neurons connected to each other.
- Neural network model trained in computer system
- Layers of neurons with forward propagation
- Neurons connected within and between layers
Potential Applications
The technology can be applied in various fields such as:
- Image recognition
- Natural language processing
- Autonomous vehicles
Problems Solved
This technology addresses challenges such as:
- Pattern recognition
- Data classification
- Prediction accuracy
Benefits
The benefits of this technology include:
- Improved accuracy
- Faster processing
- Scalability for large datasets
Potential Commercial Applications
The technology can be utilized in industries like:
- Healthcare for medical image analysis
- Finance for fraud detection
- Marketing for customer behavior analysis
Possible Prior Art
Prior art in neural network training includes:
- Backpropagation algorithm
- Convolutional neural networks
Unanswered Questions
How does this technology compare to traditional machine learning algorithms?
This technology offers more flexibility and adaptability compared to traditional algorithms, allowing for more complex patterns to be recognized.
What are the limitations of this neural network model?
The model may face challenges with overfitting, training time, and interpretability of results.
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.
