What are Vectors?
- Vectors, within the context of Data Science, represent ordered collections of numerical values endowed with both magnitude and directionality. They serve as indispensable tools for representing features, observations, and model parameters within AI-ML-DS workflows.
- In Artificial Intelligence, vectors find application in feature representation, where each dimension corresponds to a distinct feature of the dataset.
- In Machine Learning, vectors play a pivotal role in encapsulating data points, model parameters, and gradient computations during the training process. Moreover, within DS, vectors facilitate tasks like data visualization, clustering, and dimensionality reduction. Mastery over vector concepts is paramount for engaging in activities like linear algebraic operations, optimization via gradient descent, and the construction of complex neural network architectures. In Python we can represent a Vector like:
import numpy as np
# Vectors can be represented as one-dimensional arrays
vector = np.array([2, -3, 1.5])
vector
Output:
array([ 2. , -3. , 1.5])
Difference Between Scalar, Vector, Matrix and Tensor
In the context of mathematics and machine learning, scalar, vector, matrix, and tensor are all different types of mathematical objects that represent different concepts and have different properties. Here in this article, we will discuss in detail scalars, vectors, matrixes, tensors, and finally the differences between them.
Table of Content
- What is Scalar?
- What are Vectors?
- What are Matrices?
- What are Tensors?
- Scalar Vs Vector Vs Matrix Vs Tensor