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An overview of Loss function and Gradient descent in Machines Learning

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Loss Function and Gradient Descent are two of the most important concepts in Machine Learning. They are used to optimize the performance of a model by minimizing the error between the predicted output and the actual output. In this article, we will discuss what Loss Function and Gradient Descent are, how they work, and provide examples of each.

A Loss Function is a mathematical equation used to measure the difference between a predicted output and an actual output. It is used to evaluate how well a model is performing on a given task. The goal of any machine learning algorithm is to minimize this loss function so that it can accurately predict outputs for unseen data points. Common loss functions include mean squared error (MSE), cross-entropy loss, hinge loss, and logistic regression loss. To Optimize the loss function various Gradient Descent algorithms are used.

Gradient Descent is an optimization algorithm used to minimize a given Loss Function. It works by iteratively updating the parameters of a model in order to reduce the value of the Loss Function at each step. This process continues until either the Loss Function reaches its minimum value or until no further improvement can be made. Gradient Descent can be implemented using various algorithms such as Stochastic Gradient Descent (SGD), Mini-Batch Gradient Descent (MBGD), or Batch Gradient Descent (BGD).

To illustrate these concepts with an example, let’s consider a simple linear regression problem where we want to predict housing prices based on square footage. We can use MSE as our Loss Function which measures how far off our predictions are from the actual values:

MSE = 1/n * Σ(y_i - y_hat)^2

where,
n :- the number of data points.
y_i :- our predicted value for each data point i.

We can then use Gradient Descent to find optimal parameters for our model which will minimize this MSE value.

It’s also important to remember that minimizing a cost function does not guarantee good performance on unseen data; it only indicates that your model is performing well on seen data. Therefore, it’s important to evaluate your model on unseen data as well in order to get an accurate measure of its performance.

In conclusion, Loss Functions and Gradient Descent are two essential concepts in Machine Learning that allow us to optimize models so that they can accurately predict outputs for unseen data points. By understanding these concepts and how they work together, we can create more powerful machine learning models that produce better results than ever before!

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