SGD or Gradient Descent that is Stochastic is a learning and optimization algorithm that trains algorithms in ML- machine learning. It is used in neural networks, artificial intelligence and deep learning as an optimizing algorithm. The algorithm’s job is to identify the internal parameters of the model, like mean squared error or logarithmic loss that performs against some other specified performance measures. The stochastic gradient descent learning algorithm has several hyperparameters like the epoch in machine learning and batch size. These hyperparameters are integer values and appear to behave similarly, thereby causing some confusion for learners. Let’s explore their differences.

Firstly, optimization is a learning-by-searching process for algorithms. The gradient descent optimization algorithm has a “gradient”, which is the error calculation with the slope or gradient error meant to define a gradient. The term used to define descent means moving downwards along the slope until it reaches the error’s minimum level. It is also an iterative algorithm meaning that the process of searching happens over and over again in discrete multiple steps wherein each step is designed to improve model parameters slightly.

Each step of the algorithm also involves the current internal parameters set, making predictions that are sample-based and comparing its predictions to expected real outcomes forecasted, and updating the error calculations to the internal parameters of the model. This algorithm update procedure varies with different types of algorithms. Artificial and Neural networks use the algorithm with back-propagation updates. 

  1. What Is an Epoch?
  2. What Is the Difference Between Epoch and Batch?
  3. Example

1. What Is an Epoch?

An epoch in machine learning means one complete pass of the training dataset through the algorithm. This epochs number is an important hyperparameter for the algorithm. It specifies the number of epochs or complete passes of the entire training dataset passing through the training or learning process of the algorithm. With each epoch, the dataset’s internal model parameters are updated. Hence, a 1 batch epoch is called the batch gradient descent learning algorithm. Normally the batch size of an epoch is 1 or more and is always an integer value in what is epoch number.

It can also be visualized as a ‘for-loop with a specified epoch number with each loop path traversing the entire training dataset. In the for-loop is a nested for-loop that allows the loop to iterate over a specified sample number in a single batch when the samples “batch size” number is specified as one. Typical values of the number of epochs when training algorithms can run into thousands of epochs, and the process is set to continue until the model error is sufficiently minimized. Normally tutorials and examples use values like 10, 500, 100, 1000, or even larger numbers.

Line plots can be created for the training process, with the X-axis having the epoch in machine learning and the Y-axis having the skill or model error. Such line plots are called the learning curve of the algorithm and help diagnose problems such as fitting of the training set being under, over or suitably learned. epoch in the neural network

2. What Is the Difference Between Epoch and Batch?

The model gets updated when a specific number of samples are processed. This is known as the batch size of samples. The number of training dataset’s complete passes is also significant and called the epoch in machine learning number in the training dataset. Batch size is typically equal to 1 and can be equal to or less than the training dataset’s sample number. The epoch in a neural network or epoch number is typically an integer value lying between 1 and infinity. Thus one can run the algorithm for any period of time. To stop the algorithm from running, one can use a fixed epoch number and also use the factor of rate of change of model error being zero over a period of time.

Both batch size and epoch in machine learning of learning algorithms are hyper-parameters with integers as values used by the training model. These values are not found by a learning process since they are not internal parameters of the model and must be specified for the process when training an algorithm on the training dataset. These numbers are also not fixed values and, depending on the algorithm, may require trying various integer values before finding the most suitable values for the process.

3. Example

Consider this example of an epoch in machine learning. Suppose one uses a dataset that has 200 samples (where samples mean the data rows) which have 1,000 epochs and a 5 batch size to define epoch-making. The dataset then has each of the 40 batches having 5 samples, with the weights of the model being updated when each batch of 5 samples passes through. Also, one epoch in machine learning, in this case, involves 40 batches meaning the model will be updated 40 times.

Also, since the epoch number is 1,000, it means the whole dataset passes through the model, and the model itself will pass through 1.000 runs. When there are 40 batches or updates to the model, it means the training dataset has 40,000 batches being used in the process of training the algorithm on this dataset!


In discovering differences in gradient descent that is stochastic in an epoch in machine learning and batches, one can say that the gradient descent stochastic algorithm uses a dataset for training with its learning algorithm that is iterative when updating the model. The batch size is a gradient descent hyperparameter that trains the training samples numbers before the internal parameters of the model are updated to work through the batch. The epoch number is again a  gradient descent hyperparameter that defines the numbers of passes that are complete when passing through datasets under training.

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