This article is a beginner's guide to understanding the basics of linear algebra for AI and machine learning. It explains the concepts of scalars, vectors, and matrices, and how they are used in AI. The author emphasizes that understanding these concepts does not require extensive study, but rather a basic understanding to get started in AI. The article also introduces PyTorch, an open-source machine learning framework, and demonstrates how to perform element-wise operations such as addition, subtraction, multiplication, and division on vectors and matrices using PyTorch.
The article also explains the sigmoid function and the Rectified Linear Unit (ReLU) function, both of which are commonly used in machine learning. The sigmoid function is used to condense large values into a range between 0 and 1, while the ReLU function filters out negative inputs. The author concludes by promising to cover more complex operations and their applications in neural networks and machine learning in the next part of the series.
Key takeaways:
You don't need to be an expert in linear algebra to get started in AI, but you do need to know the basics.
Scalars, vectors, and matrices are fundamental concepts in linear algebra and are used in AI and machine learning.
PyTorch is a widely used open source machine learning framework that can be used to perform operations on vectors and matrices.
Element-wise operations, sigmoid function, and ReLU (Rectified Linear Unit) function are important operations in machine learning.
