In this post, we will learn what is epoch in machine learning, and purpose of epochs in machine learning and some tips to train the model or machine with the epochs for better performance.
What is Epoch?
In machine learning, an epoch refers to a complete iteration through a dataset during the training phase of a model. It is a term used to measure the progress of training and represents the number of times the entire dataset has been presented to the model for learning.
During each epoch, the model processes the input data, makes predictions, compares them to the actual labels, and adjusts it’s parameters (i.e, weights and biases) through a process called backpropagation in order to minimize the differences between the predictied and actual values. These area is to train the model to make predictions accurately on new and unseen data.
Purpose of Epoch in ML?
The main purpose of epochs in machine learning is to iterate over the entire dataset during the training phase of a model. Each epoch allows the model to update it’s internal parameters based on the training data, improving its ability to make accurate predictions .
Some of main purposes of Epochs are :-
- Model Convergence
- Generalization
- Error Reduction
- Model Optimization
- Preventing Underfitting and Overfitting
- Hyperpameter Tuning
Tips for Training :-
- Regular monitor training and track model performance.
- Adjust learning rated dynamically for optimal convergence.
- Implement techniques like early stopping to prevent overfitting.
- Experiment with different optimization techniques like SGD, Adam, and model architectures like shallow vs deep networks etc.
- Apply Regularization techniques like dropout and L2 regularization to prevent overfitting and improve model generalization.
Overall, epochs are the guiding stars that illiminate the path to model mastery in the vast expanse of machine learning. Using epochs in ML facilitates the gradual optimization of the model’s parameters, enhances its learning capabilities, improves generalization, and helps to find the right balance between underfitting and overfitting.