Blog # 52 – Multiplying Matrices: A Step-by-Step Guide with Applications in AI and Machine Learning
Learn matrix multiplication for AI & machine learning!

In the realm of linear algebra, matrix multiplication is a cornerstone operation that paves the way for various computations. This blog post will equip you with a step-by-step guide to multiplying matrices, explore the crucial prerequisites, and unveil its importance in the domains of artificial intelligence (AI) and machine learning (ML).

Unlike addition and subtraction, matrix multiplication has a specific dimensional requirement:

  • Compatible Dimensions: The number of columns in the first matrix (A) must equal the number of rows in the second matrix (B) for their product to be defined. If A is of dimension (m x n) and B is of dimension (p x q), then n (number of columns in A) must be equal to p (number of rows in B) for their product to exist.

Consider matrices A (m x n) and B (p x q), where n (number of columns in A) equals p (number of rows in B). Here's how to find their product (C):

  1. Inner Product: To calculate an element (c_ij) at any row i (1 to m) and column j (1 to q) of the resulting product matrix (C), we perform the vector dot product between row i of A and column j of B. The dot product involves multiplying corresponding items between the two vectors and summing those products.
  2. Iterate and Fill: Repeat step 1 for all elements of C by considering each row of A with each compatible column of B. This will populate the entire resultant product matrix C.

Consider matrices A and B below:

A = [[1, 2], [3, 4]] (2x2) B = [[1, 3], [2, 4]] (2x2) Compatible dimensions!

To find their product (C = A * B):

  • c_11 (first element of C) = Dot product of row 1 of A and column 1 of B = (1 * 1) + (2 * 2) = 5
  • Similarly calculate other elements using the corresponding row-column dot products.

Therefore, the resultant product matrix (C) is:

C = [[5, 11], [11, 25]]

Note: If the dimensions of A and B aren't compatible (n in A doesn't equal p in B), their product cannot be calculated.

Consider matrices E and F with the following dimensions:

E = [[1, 2, 3], [4, 5, 6]] (2 rows, 3 columns)

F = [[7, 8], (3 rows, 2 columns) [9, 10], [11, 12]]

Remember: For matrix multiplication to be defined, the number of columns in the first matrix (E) must equal the number of rows in the second matrix (F). In this case, both E and F have compatible dimensions (3 columns in E and 3 rows in F).

To find the product of E and F (denoted as C), we perform the following steps:

  1. Identify Elements in Resultant Matrix (C): The resulting matrix (C) will have dimensions equal to the number of rows in the first matrix (E) multiplied by the number of columns in the second matrix (F). Here, C will be a 2x2 matrix.
  2. Calculate Each Element of C: To calculate any element (c_ij) at row i (1 or 2) and column j (1 or 2) of the resultant matrix (C), we perform the dot product between row i of E and column j of F.
  • We need to find the dot product between row 1 of E and column 1 of F.
  • Dot product of [1, 2, 3] and [7, 9, 11] = (1 * 7) + (2 * 9) + (3 * 11) = 58

Following the same principle, calculate the remaining elements of C using the corresponding row-column dot products:

  • c_12: Dot product of row 1 of E and column 2 of F (1 * 8 + 2 * 10 + 3 * 12) = 64
  • c_21: Dot product of row 2 of E and column 1 of F (4 * 7 + 5 * 9 + 6 * 11) = 139
  • c_22: Dot product of row 2 of E and column 2 of F (4 * 8 + 5 * 10 + 6 * 12) = 154

After calculating all the elements, the resulting product matrix (C) will be:

C = [[58, 64], [139, 154]]

As you can see, the product matrix C is a 2x2 matrix where each element is calculated by performing the dot product between the corresponding row of E and column of F. This process of matrix multiplication underpins numerous applications in AI and machine learning.

Matrix multiplication forms the backbone of numerous AI and ML algorithms. Here are some key areas where it's extensively used:

  • Neural Networks: The core building block of deep learning, artificial neural networks heavily rely on matrix multiplication for propagating signals between neurons and performing computations within layers.
  • Linear Regression: This fundamental ML algorithm utilizes matrix multiplication to fit a linear model to the data and make predictions.
  • Image Recognition: In convolutional neural networks (CNNs) used for image recognition, matrix multiplication plays a crucial role in extracting features from images and performing image classification tasks.

Understanding matrix multiplication empowers you to delve deeper into the inner workings of many AI and ML models. It equips you to analyze how these models process information and make predictions.

This code demonstrates how to multiply matrices A and B using NumPy's dot function, which efficiently performs matrix multiplication. The resulting product matrix C is then printed.

As mentioned earlier, NumPy is a powerful tool for numerical computations but isn't strictly an ML/AI library itself.

This blog post has provided a step-by-step explanation of matrix multiplication, along with its significance in AI and ML. By mastering these concepts, you'll gain a deeper understanding of the mathematical foundations driving these transformative technologies.

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