What is an Epoch in Machine Learning

This write-up will focus on “Epoch”, a machine learning term. We will discuss what an Epoch is in machine learning, as well as other contingent terms such as batch and iterations. The article will also explain the clear difference between Epoch and Batch, as well as variations of the gradient descent optimization algorithms in machine learning. These are compulsory terms to know for anyone studying machine learning and deep learning, or aspiring to build a career in this sphere.

Understanding an epoch in machine learning:

The term “epoch” plays a pivotal role in the guidance of neural networks in the domain of machine learning. We often cite it in the context of optimization and model convergence. In machine learning, an epoch implies a complete cycle over the entire dataset during the course phase. To delve intensely into this fundamental concept, let’s explore candidly what an epoch entails. Additionally, let’s explore its significance in the spectrum of artificial intelligence.

What is machine learning?

Machine learning is a subset of artificial intelligence(AI) that sheds light on the development of algorithms and statistical models. The models that can enable computer systems to progressively enhance their performance on a particular task. Essentially, machine learning algorithms allow computers to learn from and make predictions and decisions depending on data. In each scenario, you can make predictions and decisions without programming.

By identifying insights and patterns within the data, machine learning algorithms can make accurate predictions as well as decisions. This technology has a huge range of applications such as image recognition and predictive analysis. The technology is more significant for being not limited to natural language processing.

What is an epoch in machine learning:

The term an epoch refers to one complete cycle through the complete dataset during the course phase of a machine learning model. In easy terms, it implies that the algorithm has seen the complete dataset once. During each epoch, the model sustains the entire dataset, and splits it into smaller batches. The model then processes these batches to update its weights and biases. The motive is to sanction the model to discover the underlying patterns and relationships within the features.

The epoch process:

1. Data batching:

The computations are made more feasible and memory-efficient by separating the dataset into batches.

2. Forward and backward propagation:

The network progresses each batch to make projections and calculates the error. The network then propagates this lapse backward to accustom the weights and biases. This happens via optimization algorithms like gradient descent.

3. Weight and Bias updates:

The calculated gradients update the parameters of the models. This helps to minimize the difference between actual output and predicted output.

4. Entire dataset iteration:

The complete dataset has been utilized, indicating the completion of one epoch, by iterating the process. Subsequent epochs involve the iteration of this process, granting the model to refer to its predictions. Therefore, the process helps in improving the performance over time.

Example of an epoch in machine learning:

Let’s explain an epoch with an example. Consider a dataset containing 200 samples. These samples take 1000 turns or 1000 epochs for the dataset to qualify the model. It has a batch size of about 5. The model reviews the model weights when it passes through each of the 40 batches, which have 5 samples. Therefore the model will be updated 40 times.

 Iteration:

An iteration is calling the total number of batches needed to finish one Epoch. The number of batches equals the sum of iterations for one Epoch.

Here is an example that can present a finer understanding of what an iteration is.

A machine learning model will take 5000 coaching examples to be trained. This large dataset can be split into smaller bits called batches.

The batch size is imagined to be 500, resulting in the creation of 10 batches. It takes 10 cycles to complete one Epoch.

What is a batch in machine learning?

A batch is a for-loop that iterates over one or more samples and builds predictions. The model correlates these predictions with the expected output values at the end of the batch. Comparing the two figures out the error and then utilizes it for the betterment of the model. Batch size is a hyperparameter that defines the number of samples taken to perform through a particular machine learning model before updating its internal model parameters.

Difference between Batch and Epoch in machine learning:

Algorithms used within each Epoch in machine learning:

Following are the variations of the gradient descent optimization algorithms that are used within each epoch in machine learning:

1. Batch gradient descent:

A training dataset can be split into multiple batches. If only a single batch exists, that entire training data is in one batch, then the learning algorithm is known as batch gradient descent.

Advantage:

Computes the gradient utilizing the entire dataset, resulting in stable as well as less noisy convergence.

Disadvantage:

Computationally cost-effective for larger datasets, as it needs the entire dataset for each iteration.

2.SGD(Stochastic Gradient Descent)

The learning algorithm is known as stochastic gradient descent when a batch is made up of an entire sample. SGD is pivotal in machine learning optimization algorithm methods because it efficiently handles large datasets.

Advantage:

SGD processes one guiding example at a time, introducing randomness that aids the algorithm in escaping local minima and speeding up convergence. Additionally, SGD permits online learning, making it suitable for streaming data.

On the whole, its efficiency and ability to navigate high-dimensional and complex spaces make it a cornerstone in tutoring machine learning models.

Advantage:

SGD processes one training example at a time. It introduces randomness that helps escape local minima and speeds up convergence.

It is mainly useful for large datasets.

Disadvantage:

The huge variance in updates may result in noisy convergence.

3. Mini-batch gradient descent:

A mini-batch gradient descent is known as the learning algorithm when the batch size is higher than one sample but lesser than the training dataset size.

Advantage:

Drives a balance by processing a small random subset(mini-batch)of data in each iteration. This gradient combines the stability of batch gradient descent and the efficacy of SGD, making it a good choice.

Disadvantage:

This requires modifying the batch size, and still the convergence can be somewhat noisy.

The choice depends on the computational resources, dataset size, and the estimated trade-off between efficiency and stability.

SGD is important when dealing with massive datasets, whereas Mini-batch GD often drives a practical balance in several scenarios.