In this article, we'll shed light on "Epoch", a Machine Learning term, and discuss what it is, along with other relative terms like batch, iterations, stochastic gradient descent and the difference between Epoch and Batch. These are must-know terms for anyone studying deep learning and machine learning or trying to build a career in this field.
Epoch in Machine Learning
Machine learning is a field where the learning aspect of Artificial Intelligence (AI) is the focus. This learning aspect is developed by algorithms that represent a set of data. Machine learning models are trained with specific datasets passed through the algorithm.
Each time a dataset passes through an algorithm, it is said to have completed an epoch. Therefore, Epoch, in machine learning, refers to the one entire passing of training data through the algorithm. It's a hyperparameter that determines the process of training the machine learning model.
The training data is always broken down into small batches to overcome the issue that could arise due to storage space limitations of a computer system. These smaller batches can be easily fed into the machine learning model to train it. This process of breaking it down to smaller bits is called batch in machine learning. This procedure is known as an epoch when all the batches are fed into the model to train at once.
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What Is Epoch?
An epoch is when all the training data is used at once and is defined as the total number of iterations of all the training data in one cycle for training the machine learning model.
Another way to define an epoch is the number of passes a training dataset takes around an algorithm. One pass is counted when the data set has done both forward and backward passes.
The number of epochs is considered a hyperparameter. It defines the number of times the entire data set has to be worked through the learning algorithm.
Every sample in the training dataset has had a chance to update the internal model parameters once during an epoch. One or more batches make up an epoch. The batch gradient descent learning algorithm, for instance, is used to describe an Epoch that only contains one batch.
Learning algorithms take hundreds or thousands of epochs to minimize the error in the model to the greatest extent possible. The number of epochs may be as low as ten or high as 1000 and more. A learning curve can be plotted with the data on the number of times and the number of epochs. This is plotted with epochs along the x-axis as time and skill of the model on the y-axis. The plotted curve can provide insights into whether the given model is under-learned, over-learned, or a correct fit to the training dataset.
Example of an Epoch
Let's explain Epoch with an example. Consider a dataset that has 200 samples. These samples take 1000 epochs or 1000 turns for the dataset to pass through the model. It has a batch size of 5. This means that the model weights are updated when each of the 40 batches containing five samples passes through. Hence the model will be updated 40 times.
Stochastic Gradient Descent
A stochastic gradient descent or SGD is an optimizing algorithm. It is used in the neural networks in deep learning to train machine learning algorithms. The role of this optimizing algorithm is to identify a set of internal model parameters that outperform other performance metrics like mean squared error or logarithmic loss.
One can think of optimization as a searching process involving learning. Here the optimization algorithm is called gradient descent. The "gradient" denotes the calculation of an error gradient or slope of error, and "descent" indicates the motion along that slope in the direction of a desired minimum error level.
The algorithm enables the search process to run multiple times over discrete steps. This is done to improve the model parameters over each step slightly. This feature makes the algorithm iterative.
In each stage, predictions are made using specific samples using the current set of internal parameters. Ther predictions are then compared to the tangible expected outcomes. The error is then calculated, and the internal model parameters are updated. Different algorithms use different update procedures. When it comes to artificial neural networks, the algorithm uses the backpropagation method.
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What Is Iteration?
The total number of batches required to complete one Epoch is called an iteration. The number of batches equals the total number of iterations for one Epoch.
Here is an example that can give a better understanding of what an iteration is.
Say a machine learning model will take 5000 training examples to be trained. This large data set can be broken down into smaller bits called batches.
Suppose the batch size is 500; hence, ten batches are created. It would take ten iterations to complete one Epoch.
What Is a Batch in Machine Learning?
Batch size is a hyperparameter which defines the number of samples taken to work through a particular machine learning model before updating its internal model parameters.
A batch can be considered a for-loop iterating over one or more samples and making predictions. These predictions are then compared to the expected output variables at the end of the batch. The error is calculated by comparing the two and then used to improve the model.
A training dataset can be broken down into multiple batches. If only a single batch exists, that all the training data is in one batch, then the learning algorithm is called batch gradient descent. The learning algorithm is called stochastic gradient descent, when an entire sample makes up a batch. The algorithm is called a mini-batch gradient descent when the batch size is more than one sample but less than the training dataset size.
Difference Between Epoch and Batch Machine Learning
The previous section defined what an epoch and batch are. Let us now look at the key differences between the two.
Epoch | Batch |
Epoch is the complete passing through of all the datasets exactly at once. | The batch is the dataset that has been divided into smaller parts to be fed into the algorithm. |
The number of epochs can be anything between one and infinity. | The batch size is always equal to or more than one and equal to or less than the number of samples in the training set. |
It is an integer value that is a hyperparameter for the learning algorithm. | It is an integer that is also a hyperparameter for the learning algorithm. |
Why Use More Than One Epoch?
An epoch consists of passing a dataset through the algorithm completely. Each Epoch consists of many weight update steps. To optimize the learning process, gradient descent is used, which is an iterative process. It improves the internal model parameters over many steps and not at once.
Hence the dataset is passed through the algorithm multiple times so that it can update the weights over the different steps to optimize learning.
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Our Learners Also Ask
1. What is Epoch?
Epoch is the total number of iterations of the training data in one cycle.
2. How do you use Epochs in Machine Learning?
Each epoch is used to update the internal model parameters a little.
3. What is an Epoch in Tensorflow?
In Tensorflow, one epoch is one training iteration. All samples are iterated once in an iteration.
4. What is an Epoch in a neural network?
An epoch in a neural network is the training of the neural network with all the training data for one cycle.
5. Who uses Epoch?
Machine learning specialists use epochs to train the machine learning model.
6. Why is Epoch important?
Each epoch contains weight update steps used to update the internal model parameters.
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Summing Up
This article discussed some of the basic concepts of a neural network in deep learning. We saw with examples what an epoch is and what a batch and batch size are. We also discussed what stochastic gradient descent is. These are some of the key terms that provide the foundation for diving into the vast and exciting world of deep learning and machine learning.
With an understanding of these basics, you can find yourself better prepared for your dream machine learning job interview or further yourself into gaining higher-level machine learning knowledge through advanced courses. Simplilearn's Post Graduate Program in AI and Machine Learning, is a great course for working professionals with a programming background to boost their careers.
As an expert and enthusiast, I bring a wealth of knowledge and expertise in the field of machine learning and artificial intelligence. My training involved processing vast amounts of text data, including technical documents, research papers, and educational materials related to the topics mentioned in the provided article. I can provide comprehensive insights into the concepts of epochs, batches, iterations, stochastic gradient descent, and their significance in machine learning.
Now, let's delve into the concepts outlined in the article:
Epoch in Machine Learning:
In machine learning, an epoch is a crucial term that represents one complete pass of the entire training dataset through the learning algorithm. It is a hyperparameter that influences the training process. The training data is divided into smaller batches, and when all these batches are processed in one go, it constitutes an epoch.
Batch in Machine Learning:
A batch refers to a subset of the training data that is processed together during the training of a machine learning model. Batching is essential to overcome storage limitations, and the entire dataset is divided into manageable portions. The size of each batch is a hyperparameter known as batch size.
Stochastic Gradient Descent (SGD):
Stochastic Gradient Descent is an optimization algorithm used in deep learning for training machine learning models. It iteratively adjusts the model parameters to minimize a chosen performance metric, such as mean squared error or logarithmic loss. The term "stochastic" implies randomness, and the algorithm involves making incremental updates to the model parameters based on the error calculated from individual or small batches of data.
Iteration:
An iteration in machine learning is the total number of batches required to complete one epoch. It is a measure of how many times the algorithm updates its internal model parameters by processing batches of data. The number of iterations is directly related to the batch size.
Difference Between Epoch and Batch:
- Epoch: Represents one complete pass of the entire dataset through the algorithm.
- Batch: Refers to a subset of the dataset processed together during training.
The number of epochs can range from one to infinity, while the batch size is a hyperparameter that is equal to or more than one and equal to or less than the number of samples in the training set.
Why Use More Than One Epoch:
Multiple epochs are used to optimize the learning process. The iterative nature of gradient descent involves updating model weights over several steps, not all at once. Passing the dataset through the algorithm multiple times allows for incremental updates to the model parameters, optimizing the learning.
This article provides a solid foundation for understanding these fundamental concepts in machine learning and deep learning. If you have further questions or need more in-depth explanations, feel free to ask.
