Backpropagation has been an integral part of machine learning since the 1980s. It is a powerful and well-known algorithm used in various types of neural networks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep learning. As powerful as it is, backpropagation can still be improved upon by introducing modern techniques. In this article, we will discuss some modifications to plain backpropagation that can improve training.
Momentum
Momentum is a technique used to improve the accuracy and speed of training in backpropagation. Momentum works by tracking the last weight change and updating the current weight change according to the gradient of the cost with respect to the weights and the momentum. This can great speed up the learning process.
Adaptive Learning Rate
Adaptive learning rate is a technique used to reduce the learning rate over time as the model gets closer to the optimal weights. There are various methods to achieve this, such as halving the learning rate every 10 epochs, inverse decay, exponential decay, and AdaGrad. AdaGrad is a more modern technique which involves keeping a cache of the weight changes so far and updating the weights according to the gradient of the cost with respect to the weights.
Regularization
Regularization is a technique used to prevent overfitting in neural networks. The two main regularization techniques are L1 and L2 regularization. L1 regularization adds the usual cost to the absolute value of the weights times a constant, while L2 regularization adds the usual cost to the square of the weights times a constant. Both of these techniques help to ensure that the weights do not go to infinity, which can lead to overfitting.
Early Stopping
Early stopping is a technique used to reduce overfitting in neural networks. It involves stopping the training process early when the cost on the validation set begins to increase. This helps to ensure that the model does not overfit to the training data.
Noise Injection
Noise injection is a technique used to prevent overfitting in neural networks. It involves adding random noise to the input data during training. This noise usually takes the form of a Gaussian-distributed random variable with zero-mean and small variance. This helps to simulate having more data and results in a more robust predictor.
Data Augmentation
Data augmentation is a technique used to increase the robustness of models. It involves creating new data and training on both the original data and the hand-crafted data. This helps the model to recognize different variations of the same data, resulting in a more robust predictor.
Dropout
Dropout is a relatively new technique used to reduce overfitting in neural networks. It involves multiplying the nodes of each layer by a bitmask which is a random array of 0’s and 1’s. This technique creates an ensemble of neural networks and helps to ensure that the model does not overfit to the training data.
Conclusion
In this article, we discussed some techniques to improve backpropagation. These include momentum, adaptive learning rate, regularization, early stopping, noise injection, data augmentation, and dropout. All of these techniques can help to improve the accuracy and speed of training in backpropagation, while also helping to reduce overfitting. We encourage you to experiment with these techniques and see what works best for your application.