Linear Algebra for AI

Name: Linear Algebra for AI
Availability: InStock
Author: FreeAcademy

Intermediate

Assumes foundational knowledge. Builds on basics.

Master linear algebra through the lens of artificial intelligence. Learn vectors, matrices, dot products, eigenvalues, and tensors by seeing exactly how they power neural networks, transformers, embeddings, and other AI systems.

Course Content

6 modules • 18 lessons

Vectors: The Language of Data

3 lessons

1.1

What Are Vectors?

1.2

Vector Operations

1.3

Word Embeddings: Vectors in Action

Matrices: How Neural Networks Think

3 lessons

2.1

What Are Matrices?

2.2

Matrix Operations

2.3

Neural Network Layers as Matrices

Matrix Multiplication: The Engine of AI

3 lessons

3.1

Matrix Multiplication Explained

3.2

Matrix Multiplication in Transformers

3.3

Computational Efficiency and GPUs

Dot Products and Similarity

3 lessons

4.1

The Dot Product

4.2

Cosine Similarity

4.3

Similarity in AI Applications

Eigenvalues and Eigenvectors

3 lessons

5.1

What Are Eigenvalues and Eigenvectors?

5.2

Principal Component Analysis (PCA)

5.3

Eigendecomposition in AI

Tensors and Multi-Dimensional Data

3 lessons

6.1

From Scalars to Tensors

6.2

Tensor Operations in Deep Learning

6.3

Linear Algebra Throughout the AI Pipeline

Reviews

Linear Algebra for AI

Intermediate

Assumes foundational knowledge. Builds on basics.

Course Content

6 modules • 18 lessons

Vectors: The Language of Data

3 lessons

1.1

What Are Vectors?

1.2

Vector Operations

1.3

Word Embeddings: Vectors in Action

Matrices: How Neural Networks Think

3 lessons

2.1

What Are Matrices?

2.2

Matrix Operations

2.3

Neural Network Layers as Matrices

Matrix Multiplication: The Engine of AI

3 lessons

3.1

Matrix Multiplication Explained

3.2

Matrix Multiplication in Transformers

3.3

Computational Efficiency and GPUs

Dot Products and Similarity

3 lessons

4.1

The Dot Product

4.2

Cosine Similarity

4.3

Similarity in AI Applications

Eigenvalues and Eigenvectors

3 lessons

5.1

What Are Eigenvalues and Eigenvectors?

5.2

Principal Component Analysis (PCA)

5.3

Eigendecomposition in AI

Tensors and Multi-Dimensional Data

3 lessons

6.1

From Scalars to Tensors

6.2

Tensor Operations in Deep Learning

6.3