- Optimization is the core of every machine learning algorithm.
- Understand how the Gradient descent algorithm works and optimize model performance.

*Note: If you are more interested in learning concepts in an Audio-Visual format, We have this entire article explained in the video below. If not, you may continue reading.*

Picture a scenario, you are playing a game with your friends where you have to throw a paper ball in a basket. What will be your approach to this problem?

Here, I have a ball and basket at a particular distance, If I through the ball with 30% of my strength, I believe it should end up in the basket. But with this force ball didnâ€™t reach the target and falls before the basket.

In my second attempt, I will use more power say 50% of my strength. This time I am sure it will reach its place. But unexpectedly ball crosses the basket and falls far from it.

Now I calculate and conclude the force should be somewhere between 30% and 50%. This time I threw the ball with 45% of my strength, and yay! I succeed. The ball directly drops into the basket. That means 45% of strength is the optimum solution for this problem. Understanding the current position and adjusting accordingly is crucial in achieving success.

This is how the gradient descent works.

Following up on the previous example letâ€™s understand the intuition behind gradient descent.Â As shown in the following image.

For a given problem statement, the solution starts with a Random initialization. These initial parameters are then used to generate the predictions i.e the output. Once we have the predicted values we can calculate the error or the cost. i.e how far the predicted values are from the actual target. In the next step, we update the parameters accordingly and again made the predictions with updated parameters.

This process will go iteratively until we reach the optimum solution with minimal cost/error.

Also Read: A Beginner’s Guide to Logistic Regression

Gradient descent is an optimization algorithm that works iteratively to find the model parameters with minimal cost or error values. At each iteration, we try to decrease the cost function in a given number of iterations. If we go through a formal definition of Gradient descent

Gradient descentis a first-orderiterative optimization algorithm for finding a local minimum of a differentiable function.

Letâ€™s consider a linear model,** Y_pred= B0+B1(x). **In this equation, Y_pred represents the output. B0 is the intercept and B1 is the slope whereas x is the input value. B0 and B1 are also called coefficients.

For a linear model, we have a convex cost function as shown in the image below that looks like a bowl.

Initially, you can start with randomly selected parameters B0 and B1 values and make the predictions Y_pred. The next step is to calculate the error i.e. the difference between the original and the predictions.

The predicted values with these parameters will land you anywhere on this cost function curve. Now the task is to update the value of B0 and B1 for minimizing the error i.e coming down on this curve to the lower points. This process will continue until we reach the lowest point of this curve i.e the minimum cost.

As by this time, we have a clear idea of Gradient descent, letâ€™s now get into the mathematics behind it and see how it actually works in a step-wise manner.

Here we are using a linear regression model. To start with a baseline model is always a great idea. In our baseline model, we are using the values 0 for B(slope of the line) and b (intercept) is the mean of all independent variables.

Now we can use this baseline model to have our predicted values y hat.

**Also Read: Out-of-Core ML: An Efficient Technique to Handle Large Datasets**

Once we have our prediction we can use it in error calculation. Here, our error metric is the mean squared error(MSE).

Mean squared error is the average squared difference between the estimated values and the actual value.

Our calculated error value of the baseline model can lead us anywhere on the Cost-B curve as shown in the following images. Now our task is to update B in such a way that it leads the error towards the bottom of the curve (i.e. global minimum).

Now the question is how the parameters (B in this case) will be updated. To update our parameters we are going to use the partial derivatives. The partial derivatives give the slope of the line, which is the gradient of the cost function, also, it is the change in the cost with respect to change in the B.

Look at the image below in each case the partial derivative will give the slope of the tangent. In the first case, the slope(i.e. direction of the gradient) will be negative whereas in the other case, the slope will be positive.

Once we have the partial derivative, we can update the values of B as shown in the image below.

Overall the whole process of updating the parameters will look like the following image. This is called backpropagation.

Another important aspect of this whole process is the learning rate (a). The learning rate is a hyperparameter that decides the course and speed of the learning of our model. This can also be called step size.

The learning rate should be an optimum value. If the learning rate will be high, the steps taken will be large and we can miss the minima. As a result, the model will fail to converge.

On the other hand, if the learning rate will be too small, The model will take too much time to reach the minimum cost with only small steps. If the learning rate is too high value of gradients explode and overshoots.

Effect of learning rate (a) too high learning rate (b) too low learning rate

Now a question arises, what if there are multiple parameters in a model? In such cases, nothing will change just the dimensions of the cost function will increase. As it becomes a 3D plot in the following image. Also, the objective will remain the same i.e reaching the global minimum.

Further, the parameters will be updated exactly like the previous case as shown below:

Hence, it doesnâ€™t matter how many parameters you have the process and objective will remain the same i.e to update the parameters to reach the minimum value of the cost function.

This step-by-step tutorial on gradient descent explains a fundamental optimization algorithm at the heart of AI. By understanding how gradient descent minimizes loss functions to find coefficients, readers can appreciate its pivotal role in training AI models efficiently and effectively. For instance, consider a training dataset of images labeled with various objects. Using gradient descent, a convolutional neural network can iteratively adjust its parameters to minimize the difference between predicted and actual labels, thus improving its ability to classify objects in new images accurately.

*If you are looking to kick start your Data Science Journey and want every topic under one roof, your search stops here. Check out Analytics Vidhyaâ€™s **Certified AI & ML BlackBelt Plus Program*.

A. Gradient descent optimizes machine learning models through different approaches: Batch Gradient Descent computes gradients for the whole dataset, Stochastic Gradient Descent updates parameters per data point for speed, and Mini-batch Gradient Descent uses small data subsets for a balance of speed and stability. Momentum, a variant of SGD, accelerates updates by incorporating past gradients.

A. Yes, gradient descent can work on multi-output functions, which are functions that return more than one output variable. In the context of machine learning and optimization, a multi-output function might represent a scenario where you’re trying to predict multiple target variables (outputs) simultaneously from a given set of input variables. Gradient descent, a popular optimization algorithm used to minimize the loss function in machine learning models, can be adapted to handle such multi-output scenarios.

A. The learning rate in gradient descent affects convergence by controlling the steepness of each steepest descent step, moving in the opposite direction of the negative gradient.

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Become a full stack data scientist
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Understanding Early stopping
Understanding Dropout
Vanishing and Exploding Gradients
Weights Initialization Techniques
Implementing Weight Initializing Techniques
Batch Normalization
Image Augmentation Techniques
Image Generator and Fit Generator
Model Checkpointing
Implementing Model Checkpointing
Dealing with Class Imbalance
Ensemble Deep Learning
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