What is an Epoch in Machine Learning?

With the digital era booming into fruition, many have begun searching for relative insight into this extensively evolving field of Machine Learning. Machine Learning has beyond doubt led to a series of advancements in the world of technology and is continuing to do so. If you are just starting out with Machine Learning, I am sure that at some point you have come across the word ‘epoch’, and wondered what it means. So, how exactly can an epoch be defined within the context of Machine Learning?

Within the context of Machine Learning, an Epoch can be described as one complete cycle through the entire training dataset and indicates the number of passes that the machine learning algorithm has completed during that training.

This fascinating and continuously developing concept has been widely speculated and investigated, and the exact use of an epoch is subject to the context in which it is being used. In this article, with the help of some conclusive examples that I gathered during my research, I will try to try to explain what an epoch in machine learning entails in an easy to understand manner. If you are interested in learning more on epoch, I am sure that you will find this article interesting. So, read on.  

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What is the Use of Epoch? 

Machine learning frequently requires hundreds or thousands of epochs, and the number of epochs used within any model represents a hyperparameter. In essence, a hyperparameter defines the number of times that the learning algorithm will run through the dataset. This allows the learning algorithm to run until the error or area of improvement within the model has been recognized or sufficiently optimized.

The specific use of an epoch is predominantly subject to the area of machine learning that it’s being applied to. When this is applied to reinforcement learning, where it’s typically referred to as an episode, the agent will be primarily learning which decisions to make in comparison with the consequences of each, and it may not take the same route to complete the same task.

Within a neural network, the goal of the model would be based on classifying or generating material that is definitively right or wrong. An epoch required for experimental agents that perform various actions for a single task may therefore differ when compared with an epoch for agents attempting to perform a single action for many similar tasks. 

Epochs are also used to gather and group performance data relating to the development of the machine learning model in the form of a line plot. It’s common to use epochs along the x-axis as a representation of time, and use the y-axis to display ability improvement or lack thereof. These line plots are often called learning curves, and are used in determining whether the model has learned or not, and whether the model is suitably fit to the training data set and intended outcomes.

What is an Epoch in Machine Learning? 

Machine learning entails the use of advanced algorithms that analyze data, learn from it, and utilize these learning points in order to identify patterns of interest. Multiple epochs are used throughout the development of machine learning models, and since this involves learning according to what is learned from the dataset, some human intervention is required during the initial stages. 

There are two types of machine learning models, and they can be classified as either supervised or unsupervised learning models. The learning capacity of these models requires specific datasets in order to develop, and these training datasets need to be planned according to the outcome that is hoped to be achieved as well as the task(s) that the agent will need to perform.

When one attempts to thoroughly define an epoch, which can primarily be understood as one cycle through the full training dataset, one must understand the basis and underlying terms that form an epoch within this context. An epoch is ultimately composed of data batches and iterations, the sum of which will ultimately amount to an epoch.

Machine learning as a whole is primarily based on data within its various forms. Each dataset is composed of a certain amount of samples or rows of data which is subject to the objective and context of the data.

The dataset itself is usually grouped into batches, particularly when the dataset is extensive or large. The size of the batch can be identified as a hyperparameter which outlines the number of samples to work with prior to updating the internal model parameters. These batches are then run through iterations, which refers to the number of batches or steps through segregated packets of the training data necessary to complete one epoch. 

At the end of the iteration of each batch, predictions are assessed in comparison with the expected output variables, and error is calculated. This calculated error is then used in order to update the algorithm and improve the model.

One epoch entails a full cycle of the training dataset which is composed of dataset batches and iterations, and the number of epochs required in order for a model to run efficiently is based on the data itself and the goal of the model. While there is no guarantee that a network will converge through the use of data for multiple epochs, machine learning will most likely require multiple epochs to achieve the desired outcome. 

If one considers the purpose of an epoch within machine learning in comparison to an everyday life scenario, we can find similar examples that may create a clearer picture of these abstract concepts and how they relate to each other. 

For example, if we consider traveling from point A to point B as the ‘task’, we could consider any available route from A to B as an ‘epoch’, and the specific route details such as stops and turns as the ‘iterations’.

For a more numerical example, one can consider a hypothetical dataset. Let’s say that you have a dataset with 200 samples or rows of data, and you choose a batch of 5 samples, then this will be the full training dataset. Let’s say that you choose 1,000 epochs, which would equate to 1,000 cycles of this training dataset.

This would entail that the dataset should be divided into 40 batches, each containing 5 samples, with the model being updated after each iteration or batch of 5 samples. This would also entail that one epoch within this context would involve running through 40 batches or 40 iterations, and will go through 40 updates as a result. 

With one epoch being equal to 40 batches, and there is a total of 1,000 epochs or cycles through the entire dataset, in this case, the model being developed here would have run through a total of 40,000 batches or iterations throughout the entire training process. 

What is an Epoch in the Neural Network? 

A neural network consists of an array or algorithms for data modeling, which utilizes graphs of neurons used in machine learning in order to arrange algorithms so that the neural network can make accurate decisions independently. Neural networks can be classified as feedforward, recurrent, convolutional and modular. 

Similarly to the use of an epoch within machine learning models, one epoch within neural networks equates to one full training cycle iteration on the training dataset. But, the epoch itself will differ from the epoch of a machine learning model as a result of the goal of the model. 

Much like machine learning models, it can take thousands of epochs or full cycles of training datasets before the neural network can learn and behave as it was intended to, and the complexity of the neural network will also have an impact on this process.

The primary difference between machine learning and neural networks is the fact that neural networks do not require human intervention. This is due to the fact that the various layers pass the data through interconnected hierarchies of nodes, as each node classifies the data of previous layers before passing it onto the next layer. 

By using multiple epochs in different patterns, the neural network being trained will hopefully develop better generalization when provided with input test data. It gives the network a chance to see previous data and readjust the internal model parameters to ensure the model is unbiased against the last data points undergone during training.

If a neural network has more layers, the computational and problem-solving abilities of that network will be greater. This process allows neural networks to develop the ability to learn through its own errors, as opposed to solely learning through sample datasets like machine learning models do.

Epochs have been utilized widely across this extensive field, and each epoch is created in direct correlation with the type of model being developed, and the purpose of the model in comparison with the sample dataset it’s given. While there is no specific amount of epochs that would equate to an efficient model, it’s important to understand the benefit of each and every epoch in updating the development of the model. 

The model will generally be considered as successful in its learning ability if there was satisfactory learning in relation to the provided epoch data training set, and if the overall process resulted in errors being identified and sufficiently minimized.

Conclusion 

To conclude, in Machine Learning, an epoch is defined as one complete cycle through the training dataset and indicates the number of passes that the machine learning algorithm has completed during the training. In simple terms, if you consider travelling from point A to point B as a task, then the number of available routes from A to B would be described as epoch in. 

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