Lecture 9 - Approx/Estimation Error & ERM | Stanford CS229: Machine Learning (Autumn 2018) Published 2020-04-17 Download video MP4 360p Recommendations 1:20:41 Lecture 10 - Decision Trees and Ensemble Methods | Stanford CS229: Machine Learning (Autumn 2018) 1:19:34 Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018) 13:11 ML Was Hard Until I Learned These 5 Secrets! 1:23:26 Lecture 8 - Data Splits, Models & Cross-Validation | Stanford CS229: Machine Learning (Autumn 2018) 1:44:31 Stanford CS229 I Machine Learning I Building Large Language Models (LLMs) 20:42 NATO Secretary General with the President of Ukraine 🇺🇦 Volodymyr Zelenskyy, in Kyiv, 03 OCT 2024 10:01 AI, Machine Learning, Deep Learning and Generative AI Explained 12:13 PCA 58:12 MIT Introduction to Deep Learning | 6.S191 24:02 The Race to Harness Quantum Computing's Mind-Bending Power | The Future With Hannah Fry 1:18:17 Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018) 17:57 Generative AI in a Nutshell - how to survive and thrive in the age of AI 1:18:55 Lecture 13 - Debugging ML Models and Error Analysis | Stanford CS229: Machine Learning (Autumn 2018) 33:04 The Grandfather Of Generative Models 1:49:28 General Relativity Lecture 1 36:54 Prof. Geoffrey Hinton - "Will digital intelligence replace biological intelligence?" Romanes Lecture 1:15:20 Stanford CS229: Machine Learning Course, Lecture 1 - Andrew Ng (Autumn 2018) 1:20:25 Lecture 7 - Kernels | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018) Similar videos 1:20:14 Lecture 11 - Introduction to Neural Networks | Stanford CS229: Machine Learning (Autumn 2018) 1:20:15 Lecture 18 - Continous State MDP & Model Simulation | Stanford CS229: Machine Learning (Autumn 2018) 1:16:38 Lecture 12 - Backprop & Improving Neural Networks | Stanford CS229: Machine Learning (Autumn 2018) 1:12:43 RL Debugging and Diagnostics | Stanford CS229: Machine Learning Andrew Ng - Lecture 20 (Autumn 2018) 1:18:52 Lecture 5 - GDA & Naive Bayes | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018) 1:51:12 Stanford CS229: Machine Learning | Summer 2019 | Lecture 9 - Bayesian Methods - Parametric & Non 1:14:19 Lecture 9 | Machine Learning (Stanford) 23:50 Statistical Machine Learning Part 6 - Risk minimization, approximation and estimation error 1:18:10 Lecture 16 - Independent Component Analysis & RL | Stanford CS229: Machine Learning (Autumn 2018) 1:22:02 Lecture 4 - Perceptron & Generalized Linear Model | Stanford CS229: Machine Learning (Autumn 2018) 1:19:48 Lecture 15 - EM Algorithm & Factor Analysis | Stanford CS229: Machine Learning Andrew Ng -Autumn2018 1:20:31 Lecture 14 - Expectation-Maximization Algorithms | Stanford CS229: Machine Learning (Autumn 2018) More results