Last updated: 22nd April, 2024
This post will teach you about the gradient descent algorithm and its importance in training machine learning models. For a data scientist, it is of utmost importance to get a good grasp on the concepts of gradient descent algorithm as it is widely used for optimizing/minimizing the objective function / loss function / cost function related to various machine learning models such as regression, neural network, etc. in terms of learning optimal weights/parameters. This algorithm is essential because it underpins many machine learning models, enabling them to learn from data by optimizing their performance.
The gradient descent algorithm is an optimization algorithm used to minimize an objective function, commonly called the cost or loss function in machine learning. This cost function is a mathematical function used to calculate loss or error which is the difference between the model’s predicted value and the actual value in the real world for a given input data set. While training a model, the ask is to find a high-performance model (model with a minimal loss). To find the best model, we need to minimize the cost function. And, minimizing the cost function is about finding the most optimal values of parameters or weights vector that give the minimal loss for any given input data set. The algorithm used to minimize the cost function is called the gradient descent algorithm.
The term “gradient descent” is derived from two key concepts: “gradient” and “descent.”
Thus, “gradient descent” is aptly named as it describes the process of descending (minimizing) a function by moving against (opposite to) its gradient. The following plot can be used to understand the gradient descent algorithm.
The plot visualizes the concept of gradient descent on a simple quadratic function f(x)=x2. The red dots represent the steps taken by the gradient descent algorithm starting from an initial point (here, x=9) and moving towards the minimum of the function at x=0.
Each step is determined by the gradient (slope) of the function at that point, and the algorithm iteratively moves in the direction that reduces the function’s value (i.e., descending). The red dashed line connects these steps, illustrating the path taken by the algorithm as it seeks the function’s minimum. This exemplifies how gradient descent navigates the function’s landscape to find the point of lowest value.
Calculating gradient descent involves several steps aimed at iteratively finding the minimum of a function. Here’s an expanded explanation with examples for better understanding:
Gradient descent algorithms could be implemented in the following two different ways:
In batch gradient descent, the update of model parameters or weights is calculated using the entire training dataset at once. This means that for each step in the training process, the algorithm calculates the gradient of the cost function for the entire dataset. The idea is to find the overall direction of the cost function at each step.
This algorithm is often used when the training dataset is relatively small and can fit into memory comfortably. It’s also suitable for less complex models where the cost of computing the gradient for the whole dataset is not prohibitively high. For example, consider a linear regression model where you’re predicting housing prices based on features like size and location. If your dataset is small, you could use batch gradient descent to train your model, updating the weights based on the error calculated from the entire dataset.
Stochastic gradient descent updates the model’s parameters using only one training example or a small batch of examples at a time. This means the gradient and hence the parameter update is calculated and applied after each example or small batch, making the process more incremental.
SGD is particularly useful when dealing with large datasets that cannot fit into memory. It’s also favored when training complex models, as it can handle the high computational cost more efficiently. Additionally, the stochastic nature of SGD can help the model escape local minima, potentially leading to better solutions. For example, in a deep learning model for image classification with a massive dataset, using SGD would be practical. Here, the model parameters are updated incrementally as each image (or a small batch of images) is processed, enabling the model to learn progressively without the need to load the entire dataset into memory.
The following code represents the python code for implementing and plotting gradient descent algorithm:
import numpy as np import matplotlib.pyplot as plt # Defining a simple quadratic function and its derivative f = lambda x: x**2 f_prime = lambda x: 2*x # Generating values x = np.linspace(-10, 10, 400) y = f(x) # Gradient descent settings learning_rate = 0.15 x_start = 9 # Starting point steps = [x_start] n_iterations = 15 # Gradient Descent Iteration for _ in range(n_iterations): x_start = x_start - learning_rate * f_prime(x_start) steps.append(x_start) # Plotting the function plt.figure(figsize=(10, 6)) plt.plot(x, y, lw=2, label="f(x) = x²") # Plotting the steps plt.scatter(steps, f(np.array(steps)), color='red', s=100, label='Steps') plt.plot(steps, f(np.array(steps)), color='red', linestyle='--', lw=1.5) # Annotations and labels plt.title('Gradient Descent on f(x) = x²') plt.xlabel('x') plt.ylabel('f(x)') plt.legend() plt.grid(True) plt.show()
As a summary, you learned the concepts of Gradient Descent along with some of the following aspects:
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