Stanford CS229: Machine Learning | Summer 2019 | Lecture 1 - Introduction and Linear Algebra

Показать описание

Anand Avati
Computer Science, PhD

To follow along with the course schedule and syllabus, visit:

Рекомендации по теме

Комментарии

I am an international student from Taiwan. I love this course! Thanks for your amazing course, Dr. Anand Avati!

Hugo_Musk

it's amazing how we can get the lecture of one of the best ml engineers for wow

zappist

mind-blowingly simple way of explaining otherwise such complex concepts - the transformation from row space to column space discussion was amazingly intuitive. Thanks a ton and now planning to see all the remaining lectures.

manishanandani

For me: This is the lecture for me. Accomplish all the prerequisites.

desireinHuman

This was a great tutorial. I understood most of the things clearly with very little pain. Your teaching method is amazing Dr Anand. Looking forward to watching all the lectures in the series. Thank you!

Tomharry

I think at 46:40 the explanation for a rank 1 matrix should be that the columns of the resulting matrix are just scaler multiples of the first column. Hence the dimension of the column space is 1.

VigyanBharat

Camera man has done a bad job. When speaker tries to explain something on the board, the focus is still on the speaker.

prasannajoshi

Brilliant lecture. I love the mathematical intuitions rather than jumping head first into neural nets. I would recommend playing at 1.25x speed though. Any chance we can get our hands on the homeworks / assignments with solutions?

gauravmenghani

Bonjour! I love to learn from you.

Thank you so much!

april

thank you! any plan to release recent years' ML courses as well?

waynep

Oh my god....
(Brilliant & awesome)^n, n->infinity

sajolsajol

Great Lecture. One question in last part 1:44:30, why we are getting only ellipsoid output for sphere shape input, not any other shape? Does it have to do something with the assumption of A being symmetric?

bhiman

May i know the course mathematics which is ore requisite for this course

ambarkumar

Is there a specific relationship between column space of a matrix and number of eigen vectors? Can the latter be greater than the former?

Is there a relationship between rank, columnspace and eigenspace?

anandvamsi

Hi, I am in Bachelor, can i continue this course ?

AnuragKumar-lvul

is this course for bachelor's level or master's?

BangtanArmy-mtph

why Raj Koothrappali is teachin in standford
???

sapito

an "ermm" for at least every minute?

PanZheng

I know i'm not that intellectually gifted, but how do students focus on the lecture for 2hrs straight 💀💀

newbie

Stanford CS229: Machine Learning | Summer 2019 | Lecture 1 - Introduction and Linear Algebra

Stanford CS229: Machine Learning | Summer 2019 | Lecture 1 - Introduction and Linear Algebra

Andrew Ng's Secret to Mastering Machine Learning - Part 1 #shorts

Stanford CS229: Machine Learning | Summer 2019 | Lecture 5 - Perceptron and Logistic Regression

Stanford CS229: Machine Learning | Summer 2019 | Lecture 2 - Matrix Calculus and Probability Theory

Stanford CS229: Machine Learning | Summer 2019 | Lecture 16 - K-means, GMM, and EM

Stanford CS229: Machine Learning | Summer 2019 | Lecture 6 - Exponential Family & GLM

Stanford CS229: Machine Learning | Summer 2019 | Lecture 8 - Kernel Methods & Support Vector Mac...

Stanford CS229: Machine Learning | Summer 2019 | Lecture 22 - Practical Tips and Course Recap

Stanford CS229: Machine Learning | Summer 2019 | Lecture 23 - Course Recap and Wrap Up

Stanford CS229: Machine Learning | Summer 2019 | Lecture 14 - Reinforcement Learning - I

Stanford CS229: Machine Learning | Summer 2019 | Lecture 4 - Linear Regression

Is the Machine Learning Specialization ACTUALLY Worth It? (Andrew Ng)

Stanford CS229: Machine Learning | Summer 2019 | Lecture 9 - Bayesian Methods - Parametric & No...

Advice for machine learning beginners | Andrej Karpathy and Lex Fridman

StanFord CS229: Machine Learning (Autumn 2018)| Lecture 1: Welcome! | Re-up (1080p)

Stanford CS229: Machine Learning | Summer 2019 | Lecture 18 - Principal & Independent CA

Andrew Ng: Opportunities in AI - 2023

Stanford CS229: Machine Learning | Summer 2019 | Lecture 19 - Maximum Entropy and Calibration

Lecture 1 - Stanford CS229: Machine Learning - Andrew Ng (Autumn 2018, Removed Silence)

Stanford CS229: Machine Learning | Summer 2019 | Lecture 3 - Probability and Statistics

Stanford CS229: Machine Learning | Summer 2019 | Lecture 20 - Variational Autoencoder

How I’d learn ML in 2024 (if I could start over)

How I would learn Machine Learning (if I could start over)

Stanford CS229: Machine Learning | Summer 2019 | Lecture 10 - Deep learning - I