Duration: (45:4) ?Subscribe5835 2025-02-10T08:31:09+00:00
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 15 - multiclass classification
(33:48)
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 3 - predictors
(1:1:30)
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 7 - constant predictors
(50:53)
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 5 - features
(1:13:40)
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 17-erm for probabilistic classif.
(37:)
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 11 - neural networks
(37:36)
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 19 - principal components analysis
(45:4)
Stanford CS229 I Machine Learning I Building Large Language Models (LLMs)
(1:44:31)
Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018)
(1:18:17)
Stanford CS25: V4 I Overview of Transformers
(1:17:29)
Lecture 10 - Decision Trees and Ensemble Methods | Stanford CS229: Machine Learning (Autumn 2018)
(1:20:41)
Stanford CS109 I Conditional Probability and Bayes I 2022 I Lecture 4
(1:14:37)
Stanford AA228/CS238 Decision Making Under Uncertainty I Policy Gradient Estimation \u0026 Optimization
(45:47)
Stanford CS224N NLP with Deep Learning | 2023 | Lecture 16 - Multimodal Deep Learning, Douwe Kiela
(1:18:23)
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers
(1:4:48)
Statistics for Data Science | Probability and Statistics | Statistics Tutorial | Ph.D. (Stanford)
(7:12:51)
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 1 - course information
(4:1econd)
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 13 - erm for classifiers
(35:21)
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 9 - house prices example
(38:39)
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 12 - classifiers
(55:53)
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 10 - non quadratic regularizers
(50:12)
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 14 - Boolean classification
(40:48)
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture - 2 overview
(39:31)
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 4 - validation
(48:26)
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 6 - empirical risk minimization
(1:4:34)
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 8 - non quadratic losses
(39:8)
Stanford EE104: Intro to Machine Learning | 2020 | Lecture 16 - probabilistic classification
(43:45)