What is Epoch in Machine Learning?

Introduction

The learning component of artificial intelligence (AI) is indeed the focus of the area of machine learning. Algorithms that represent a set of data are used to create this learning component. To train machine learning models, certain datasets are sent through the algorithm.

This article will define the term "Epoch," which is used in machine learning, as well as other related topics like iterations, stochastic gradient descent. Anyone studying deep learning and machine learning or attempting to pursue a career in this industry must be familiar with these terms.

Epoch in ML

In machine learning, an epoch is a complete iteration through a dataset during the training process of a model. It is used to measure the progress of a model's learning, as the number of epochs increases, the model's accuracy and performance generally improves.

During the training process, a model is presented with a set of input data, called the training dataset, and the model's goal is to learn a set of weights and biases that will allow it to accurately predict the output for unseen data. The training process is done by adjusting the model's weights and biases based on the error it makes on the training dataset.

An epoch is a single pass through the entire training dataset, in which all the examples are used to adjust the model's weights and biases. After one epoch, the model's weights and biases will be updated, and the model will be able to make better predictions on the training data. The process is repeated multiple times, with the number of repetitions being referred to as the number of epochs.

The number of epochs is a hyper parameter, which means that it is a value that is set by the user and not learned by the model. The number of epochs can have a significant impact on the model's performance. If the number of epochs is too low, the model will not have enough time to learn the patterns in the data, and its performance will be poor. On the other hand, if the number of epochs is too high, the model may over-fit the data, meaning that it will perform well on the training data but poorly on unseen data.

Determination of Epoch

One way to determine the optimal number of epochs is to use a technique called early stopping. This involves monitoring the model's performance on a validation dataset, which is a set of data that the model has not seen before. If the model's performance on the validation dataset stops improving after a certain number of epochs, the training process is stopped, and the model's weights and biases are saved. This prevents the model from overfitting the training data.

Another way to determine the optimal number of epochs is to use a technique called learning rate scheduling. This involves decreasing the learning rate, which is the rate at which the model's weights and biases are updated, as the number of epochs increases. A high learning rate can cause the model to overshoot the optimal solution, while a low learning rate can cause the model to converge too slowly.

In general, the number of epochs required to train a model will depend on the complexity of the data and the model. Simple models trained on small datasets may require only a few epochs, while more complex models trained on large datasets may require hundreds or even thousands of epochs.

Example of Epoch

Let's use an illustration to clarify Epoch. Think about a dataset with 200 samples. These samples require the dataset to go through the model 1000 times, or 1000 epochs. The batch size is five. This indicates that the model weights are modified after each of the 40 batches, each of which contains five samples. Consequently, 40 updates will be made to the model.

Stochastic Gradient Descent

Stochastic gradient descent, or SGD, is an algorithm for optimization. It is employed in deep learning neural networks to train machine learning algorithms. This optimizing algorithm's job is to find a set of internal model parameters that perform better than other performance indicators like mean squared error or logarithmic loss.

The process of optimization can be compared to a learning-based search. Gradient descent is the name of the optimization algorithm used here. The terms "gradient" and "descent" refer to movement down a slope in the direction of a desired minimal error level, respectively. The terms "gradient" describes the calculation of an error gradient or slope of error.

The search process can be repeated over distinct steps thanks to the algorithm. The goal of doing this is to marginally enhance the model parameters with each phase. The algorithm is iterative because of this property.

Predictions are made at each stage utilising samples and the existing internal parameters. Then, the forecasts are contrasted with the actual anticipated results. The internal model parameters are then modified after calculating the error. Different algorithms employ various update techniques. The backpropagation method is what the algorithm employs when it comes to artificial neural networks.

Iteration

An iteration is the total number of batches necessary to finish one epoch. The total convergence rate for one Epoch is equal to the number of batches.

Here is an illustration that can help explain what an iteration is.

Let's say that training a machine learning model requires 5000 training instances. It is possible to divide this enormous data set into smaller units known as batches.

If the batch size is 500, ten batches will be produced. One Epoch would require ten iterations to finish.

Conclusion

In conclusion, an epoch is a single pass through the entire training dataset during the training process of a model. It is used to measure the progress of a model's learning and the number of epochs can have a significant impact on the model's performance. Determining the optimal number of epochs requires techniques such as early stopping and learning rate scheduling. The number of epochs required to train a model will depend on the complexity of the data and the model.