Introduction: Backpropagation Driving Deep Learning, Its Limitations and Challenges
Backpropagation, a core algorithm that enables the training of deep learning models, has contributed to solving complex problems by efficiently adjusting the weights of artificial neural networks. However, with the advent of deep neural networks, the limitations of the backpropagation algorithm have become apparent, highlighting the need for new AI technology stacks. As of 2025, backpropagation still plays a vital role, but it is expected to be replaced by more advanced forms or entirely new methods in the future.
Core Concepts and Principles
The backpropagation algorithm works by backpropagating the error generated in the output layer towards the input layer, updating each weight. In this process, gradient descent is used to adjust the weights in the direction that minimizes the error. Backpropagation is an essential technique for training complex neural network models such as multilayer perceptrons (MLPs).
Step-by-Step Operation of Backpropagation
Backpropagation can be broadly divided into two stages: forward propagation and backward propagation. In the forward propagation stage, the input value passes through the neural network and a predicted value is calculated. In the backward propagation stage, the weights are updated based on the error between the predicted value and the actual value. By repeating this process, the neural network gradually performs more accurate predictions.
Mathematical Foundations of Backpropagation Algorithm
The backpropagation algorithm is based on differentiation and the chain rule. It calculates the gradient of the error for each weight and uses this gradient to update the weights. Through these mathematical principles, backpropagation can efficiently train complex neural network models.
Latest Trends and Changes
In 2025, AI technology trends are expanding beyond generative AI to autonomous execution and complex cognition. In particular, new AI technology stacks that do not use backpropagation are being researched to overcome the limitations of deep neural networks. Model Context Protocol (MCP)-based agent collaboration and on-device AI are gaining attention.
🔍 Key Changes in 2025
- Backpropagation-Free Learning: Active research to solve the efficiency and scalability issues of deep neural networks
- Model Context Protocol (MCP): Introduction of a new communication protocol for agent collaboration
- On-Device AI: Performing AI computations on the device itself without cloud connection, enhancing privacy
Practical Application Methods
Steps for Applying Backpropagation Algorithm
To apply the backpropagation algorithm in practice, you must first design an appropriate neural network structure and prepare training data. Next, implement the forward and backward propagation stages and update the weights using gradient descent. Finally, evaluate the model's performance using validation data and adjust hyperparameters as needed.
Considerations for Practical Application
When applying the backpropagation algorithm in practice, be careful about overfitting. Overfitting is a phenomenon in which the model is excessively optimized only for the training data, and the prediction performance for new data deteriorates. To prevent this, you can use regularization techniques or dropout. In addition, the data preprocessing and feature extraction process also have a significant impact on the model's performance, so they should be performed carefully.
Expert Advice
💡 Technical Insight
Precautions When Introducing Technology: The backpropagation algorithm is very sensitive to the quality and quantity of data. It is important to secure a sufficient amount of high-quality data and go through an appropriate data preprocessing process.
Tips Based on Field Experience: It is important to try various hyperparameter combinations and carefully observe changes in model performance to find the optimal settings. In addition, monitoring the learning process using visualization tools can help identify and improve problems.
Outlook for the Next 3-5 Years: The backpropagation algorithm is expected to be gradually replaced by new learning methods. However, it will remain a core technology for deep learning model training for the time being, and hybrid learning methods used in conjunction with new technologies are expected to become mainstream.
Conclusion
The backpropagation algorithm has greatly contributed to the development of deep learning technology, but new attempts are constantly being made to overcome the limitations of deep neural networks. As of 2025, backpropagation is still an important technology, but it is expected to be replaced by more advanced forms or entirely new methods in the future. Continuously observing changes in artificial intelligence technology and actively adopting new technologies is essential for securing competitiveness. Understand the limitations of the backpropagation algorithm and establish a learning strategy for the future right now.