When you train a model, you send data through the network multiple times. Think of it like wanting to become the best basketball player. You aim to improve your shooting, passing and positioning to minimize mistakes. Similarly, machines use repeated exposure to data to recognize patterns.
This article will focus on a fundamental concept called backpropagation. After reading you will understand:
- What is backpropagation and why is it important
- Gradient descent and its type
- Backpropagation in machine learning
Let’s dive into backpropagation and its meaning.
What is backpropagation and why is it important in neural networks?
In machine learning, machines take actions, analyze mistakes and try to improve. We give the machine an input and look for a forward pass, turning the input into an output. However, the result may differ from our expectations.
Neural networks are supervised learning systems, which means they know the correct output for any given input. The machines calculate the error between the ideal and the actual output from the forward pass. While the forward pass highlights errors in prediction, it lacks intelligence if machines do not correct them. You can join a number of data science courses available online to learn in-depth about machine learning and neural networks. It is important to understand the insight of algorithms in ML and neural networks and their practical application.
After a forward pass, machines send errors as a cost value. Analysis of these errors involves updating the parameters used in forwarding to convert input to output. This process of sending the cost value back to the input is called “backcasting”. It is crucial because it helps calculate the gradients used by optimization algorithms to learn parameters.
What is the time complexity of the backpropagation algorithm?
The time complexity of the backpropagation algorithm, which refers to how long it takes to perform each step in the process, depends on the structure of the neural network. In the early days of deep learning, simple networks were of low time complexity. However, today’s more complex networks, with many parameters, have a much higher time complexity. The primary factor affecting time complexity is the size of the neural network, but other factors such as the size of the training data and the amount of data used also play a role.
In essence, the number of neurons and parameters directly affect how backpropagation works. When more neurons are involved in the forward pass (where the input moves through the layers), the time complexity increases. Similarly, in the backward pass (where the parameters are adjusted for error correction), more parameters means more time complexity.
Gradient descent
Gradient descent is like training to become a great cricketer who excels at straight hitting. During practice, you repeatedly face balls of the same length to master that specific shot and reduce the room for error. Similarly, gradient descent is an algorithm used to minimize the cost function (error margin) so that the output is as accurate as it can be. Artificial intelligence uses this gradient descent data to train models. In-depth training of artificial intelligence models is covered in many online artificial intelligence courses. Learning from online material will provide good hands-on experience in training models in ML.
But before starting training, you need the right equipment. Just like a cricketer needs a ball, you need to know the function you want to minimize (the cost function), its derivatives and current inputs, weight and bias. The goal is to get the most accurate output, and in return you get weight and bias values with the smallest margin of error.
Gradient descent is a fundamental algorithm in many machine learning models. Its purpose is to find the minimum of the cost function, which represents the lowest point or the deepest valley. The cost function helps identify errors in machine learning model predictions.
Use account, you can find the slope of the function, which is the derivative of the function with respect to the value. Knowing the slope for each weight guides you towards the lowest point in the valley. The learning rate, a hyper-parameter, determines how much you adjust each weight during the iteration process. It involves trial and error, often improved by providing multiple data sets to the neural network. A well-functioning gradient descent algorithm should decrease the cost function with each iteration, and when it cannot decrease further, it is considered to have converged.
Different types of gradient descent
Collective gradient descent
It calculates the error, but updates the model only after evaluating the entire data set. It is computationally efficient, but may not always achieve the most accurate results.
Stochastic gradient descent
Updates the model after each training example, showing detailed improvements until convergence.
Mini-Batch Gradient Descent
It is commonly used in deep learning and is a combination of Batch and Stochastic Gradient Descent. The data set is divided into small groups and evaluated separately.
Backpropagation algorithm in machine learning
Backpropagation is a type of learning in machine learning. This falls under supervised learning, where we already know the exact output for each input. This helps calculate the gradient of the loss function, showing how the expected output differs from the actual output. In supervised learning, we use a training dataset with clearly labeled data and specified desired results.
Pseudocode in the backpropagation algorithm
The pseudocode of the backpropagation algorithm serves as a basic blueprint for developers and researchers to guide the backpropagation process. Provides high-level instructions, including code snippets for essential tasks. Although the overview covers the basics, the actual implementation is usually more complex. The pseudocode outlines the sequential steps, including key components of the backpropagation process. It can be written in common programming languages like Python.
Conclusion
Backpropagation, also known as backpropagation, is a key step in neural networks that is performed during training. Computes the gradients of a cost function for learnable parameters. It is a significant topic in artificial neural networks (ANN). Thanks for reading this far, I hope you found the article informative.