3Blue1Brown
Abstract vector spaces | Essence of linear algebra, chapter 11
What is a vector space? Even though they are initial taught in the context of arrows in space, or with vectors being lists of numbers, the idea is much more general and far-reaching.
3Blue1Brown
Abstract vector spaces: Essence of Linear Algebra - Part 15 of 15
What is a vector space? Even though they are initial taught in the context of arrows in space, or with vectors being lists of numbers, the idea is much more general and far-reaching.
3Blue1Brown
Abstract vector spaces | Essence of linear algebra, chapter 15
What is a vector space? Even though they are initial taught in the context of arrows in space, or with vectors being lists of numbers, the idea is much more general and far-reaching.
Curated Video
Recommender Systems Complete Course Beginner to Advanced - Deep Learning Foundation for Recommender Systems: Embeddings and User Context
In this video, we will discuss deep neural network models that are built on the technique of factorization, and interactions between variables and embeddings are taken into account.
Curated Video
Recommender Systems: An Applied Approach using Deep Learning - Embeddings and User Context
This video focuses on collaborative filtering with the help of deep learning and neural collaborative filtering. This clip is from the chapter "Deep Learning Foundation for Recommender Systems" of the series "Recommender Systems: An...
Math Fortress
Calculus III: Two Dimensional Vectors (Level 8 of 13)
This video is a review of Two Dimensional Vectors. This video goes over properties of vector operations. Properties are also proven geometrically and algebraically.
Professor Dave Explains
Wavefunction Properties, Normalization, and Expectation Values
We are beginning to get a glimpse of quantum mechanical principles from a rigorous, mathematical perspective. Now that we know how to use operators in conjunction with wavefunctions, let's get a better sense of what wavefunctions...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Features in Data Science: Marking Facial Features
In this video, we will cover marking facial features. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects) A...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: SubSpace
In this video, we will cover SubSpace. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In this...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Closure of a Set
In this video, we will cover closure of a set. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Vector Space
In this video, we will cover vector space. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Orthonormal Basis
In this video, we will cover Orthonormal Basis. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Matrix Product
In this video, we will cover Matrix Product. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Introduction to Mathematical Foundation of Feature Selection
In this video, we will cover an introduction to mathematical foundation of feature selection. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Coordinates Versus Dimensions
In this video, we will cover coordinates versus dimensions. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Basis and Dimensions
In this video, we will cover basis and dimensions. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to...
Professor Dave Explains
Linear Transformations on Vector Spaces
How to perform linear transformations on vector spaces.
Khan Academy
Khan Academy: Orthogonal Projections: Projection Is Closest Vector in Subspace
A video lesson proving that the projection of a vector is actually the closest vector in the subspace to the original vector.