Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

We've released 10+ Linear Algebra concepts for Machine Learning! Here's one of the visualization how PCA identifies direction of maximum variance.

TensorTonic

7,839 subscribers

48,328 views • 8 months ago •via X (Twitter)

Arts Education Science & Technology

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Functions are vectors! This perspective lets us apply the tools of linear algebra to computational problems from image and geometry processing to machine learning and light transport—and provides a natural explanation for Fourier series. Let's explore:

Functions are vectors! This perspective lets us apply the tools of linear algebra to computational problems from image and geometry processing to machine learning and light transport—and provides a natural explanation for Fourier series. Let's explore:

Max Slater

30,432 views • 3 years ago

we have released more blogs today on calculus fundamentals for machine learning and their interactive simulations. some of them includes - > backprop and gradient descents > local minima/saddle points > vector fields > taylor series > jacobian and hessian > partial derivatives

we have released more blogs today on calculus fundamentals for machine learning and their interactive simulations. some of them includes - > backprop and gradient descents > local minima/saddle points > vector fields > taylor series > jacobian and hessian > partial derivatives

TensorTonic

66,968 views • 8 months ago

Could President Ruto Know the Direction of the Vote Before 10 PM?. Here's how

Could President Ruto Know the Direction of the Vote Before 10 PM?. Here's how

Ⓝⓖ'ⓞⓡⓞⓡⓘⓔⓣ🐐🇰🇪

50,194 views • 26 days ago

We've added 15 new Computer Vision problems. revise your concepts by coding them out: > max pooling > image rotation > gaussian blur kernel > sobel edge detection > image histogram > 2D convolution > IoU > Non-maximum supression etc

We've added 15 new Computer Vision problems. revise your concepts by coding them out: > max pooling > image rotation > gaussian blur kernel > sobel edge detection > image histogram > 2D convolution > IoU > Non-maximum supression etc

TensorTonic

35,683 views • 6 months ago

The math behind Machine Learning. You'll never forget the fundamentals after this. Coming soon.

The math behind Machine Learning. You'll never forget the fundamentals after this. Coming soon.

TensorTonic

286,402 views • 8 months ago

We had the housewarming party a couple of weeks back. It was tons of fun and should hopefully give you a better idea of where we're spending some of our time improving model architectures and systems 00:00: Introduction 00:40: Jerry Tworek (), Building the world’s most automated AI lab 12:10: James Demmel (UC Berkeley), Dense Numerical Linear Algebra: A brief history and current trends 42:11: Christoph Klein (NVIDIA), Dense Linear Algebra at NVIDIA 47:54: Mark Saroufim (), Linear Algebra Kernels for the Age of Research 51:43: Ben (Tilde), One Layer Deeper…

We had the housewarming party a couple of weeks back. It was tons of fun and should hopefully give you a better idea of where we're spending some of our time improving model architectures and systems 00:00: Introduction 00:40: Jerry Tworek (), Building the world’s most automated AI lab 12:10: James Demmel (UC Berkeley), Dense Numerical Linear Algebra: A brief history and current trends 42:11: Christoph Klein (NVIDIA), Dense Linear Algebra at NVIDIA 47:54: Mark Saroufim (), Linear Algebra Kernels for the Age of Research 51:43: Ben (Tilde), One Layer Deeper…

Mark Saroufim

57,909 views • 1 month ago

Suno just released a new model and I'm finding it to be a big shocking improvement where it's becoming very hard to detect the hints that it's AI music. Here's "I am actually scared of linear algebra"

Suno just released a new model and I'm finding it to be a big shocking improvement where it's becoming very hard to detect the hints that it's AI music. Here's "I am actually scared of linear algebra"

Andy Masley

37,169 views • 5 months ago

We've just released a new video tutorial on how to run protein–ligand docking (one of our most popular workflows). Here's some highlights:

We've just released a new video tutorial on how to run protein–ligand docking (one of our most popular workflows). Here's some highlights:

Rowan

11,348 views • 6 months ago

Module for Graph Theory in Machine Learning is live! this includes fundamentals to - > computational graph (how pytorch works) > Graph representation > GNNs > Message Passing in GNNs > Spectral graph theory > random walks & markov chains

Module for Graph Theory in Machine Learning is live! this includes fundamentals to - > computational graph (how pytorch works) > Graph representation > GNNs > Message Passing in GNNs > Spectral graph theory > random walks & markov chains

TensorTonic

22,040 views • 8 months ago

Here's how One Nation will get the maximum return for Australia from our natural resources.

Here's how One Nation will get the maximum return for Australia from our natural resources.

Pauline Hanson 🇦🇺

31,852 views • 3 months ago

if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

ℏεsam

109,447 views • 1 year ago

a playlist of 30 youtube videos to learn machine learning fundamentals from scratch if you're struggling on where to start learning ML, this list goes this "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

a playlist of 30 youtube videos to learn machine learning fundamentals from scratch if you're struggling on where to start learning ML, this list goes this "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

ℏεsam

117,748 views • 1 year ago

One of the biggest "tricks" in algebra is "slide and divide." I honestly didn't understand why it worked until around 20 years after algebra class. Here's why it works.

One of the biggest "tricks" in algebra is "slide and divide." I honestly didn't understand why it worked until around 20 years after algebra class. Here's why it works.

Howie Hua

21,858 views • 6 months ago

Here's a recap of all of the alternate skins we've revealed from Hallowed Concepts. Whose do you want to see next? Or should we save the rest to be discovered?

Here's a recap of all of the alternate skins we've revealed from Hallowed Concepts. Whose do you want to see next? Or should we save the rest to be discovered?

Risk of Rain

42,420 views • 17 days ago

New episode! Michael Nielsen joins tylercowen to discuss the beauty of the universe, quantum computing, Simone Weil, finding good collaborators, and...the status of linear algebra? COWEN: Is the status of linear algebra rising? NIELSEN: [laughs] That’s a great question. It probably has, yes. COWEN: It’s prominent in quantum, right? It’s prominent in AI. NIELSEN: Google is built on matrix multiplication. It’s prominent for a lot of reasons. COWEN: What should we infer from that about the whole nature of the world?

New episode! Michael Nielsen joins tylercowen to discuss the beauty of the universe, quantum computing, Simone Weil, finding good collaborators, and...the status of linear algebra? COWEN: Is the status of linear algebra rising? NIELSEN: [laughs] That’s a great question. It probably has, yes. COWEN: It’s prominent in quantum, right? It’s prominent in AI. NIELSEN: Google is built on matrix multiplication. It’s prominent for a lot of reasons. COWEN: What should we infer from that about the whole nature of the world?

Conversations with Tyler

94,765 views • 2 years ago

HOW TO ANALYZE DIFFERENT MARKET SECTORS & FIND SECTOR ROTATION🔄 This is one of the most important concepts for beginner traders to understand. The market moves in cycles. Money flows from one area to another Here's a simple breakdown:

HOW TO ANALYZE DIFFERENT MARKET SECTORS & FIND SECTOR ROTATION🔄 This is one of the most important concepts for beginner traders to understand. The market moves in cycles. Money flows from one area to another Here's a simple breakdown:

ThiccTeddy

18,965 views • 1 month ago

Here's how the final play sounded on Classic Rock TK99. So much fun to be in the Dome today for one of the wildest games we've ever seen. Sound up for all the Woooooosssssss!!!

Here's how the final play sounded on Classic Rock TK99. So much fun to be in the Dome today for one of the wildest games we've ever seen. Sound up for all the Woooooosssssss!!!

Tim Leonard

13,648 views • 1 year ago

Security is a must for the development of AI and machine learning — and vice versa Dawn Song, Founder of Oasis Labs, explains how AI models can benefit security research View the full interview — where Dawn shares her journey into AI, Machine Learning, and web3 — below:

Security is a must for the development of AI and machine learning — and vice versa Dawn Song, Founder of Oasis Labs, explains how AI models can benefit security research View the full interview — where Dawn shares her journey into AI, Machine Learning, and web3 — below:

Oasis Labs

33,169 views • 3 years ago

One Of The Official Released Collab Of 1D JLS & One Direction Wishing On A Star.. Incase anyone don't know about this.

One Of The Official Released Collab Of 1D JLS & One Direction Wishing On A Star.. Incase anyone don't know about this.

1D Fbi

56,658 views • 1 year ago

hello 👋 we've just released the instrumentals from our first album to celebrate it's 5th birthday 🏄 here's a production breakdown of how we shaped the drums on Something For Your M.I.N.D. 👨🏻‍🔬 more to follow 👀

hello 👋 we've just released the instrumentals from our first album to celebrate it's 5th birthday 🏄 here's a production breakdown of how we shaped the drums on Something For Your M.I.N.D. 👨🏻‍🔬 more to follow 👀

SUPERORGANISM

10,106 views • 3 years ago