Machine Learning Course
Comprehensive exploration of machine learning fundamentals, covering linear models, neural networks, SVMs, and key principles for effective algorithm implementation and evaluation.
Neural Networks Demystified
In this short series, we will build and train a complete Artificial Neural Network in python.
Rational Ignorance - Optimal Learning from Complex Mechanistic Models
Explore optimal Bayesian learning from complex models, challenging traditional approaches and proposing a new method to maximize expected information while avoiding bias in multiparameter systems.
Intro to Machine Learning
Dive into machine learning fundamentals with engaging videos covering models, data exploration, validation, and advanced concepts like random forests and AutoML.
Regularization of Big Neural Networks
Explore regularization techniques for large neural networks, including DropOut, DropConnect, and stochastic pooling. Learn about deconvolutional networks and their applications in computer vision tasks.
Training Neural Networks - Part II - Lecture 11
Explore advanced neural network training techniques, including loss functions, learning rates, momentum, and strategies to prevent overfitting for improved computer vision model performance.
The Price of Implicit Bias in Robust Machine Learning
Explore the impact of implicit bias on robust machine learning, examining challenges in neural network performance and generalization gaps in worst-case scenarios.
Does Generalization Imply Accuracy on the Line? A New Look at Robust Generalization
Explore the relationship between generalization and accuracy in machine learning, examining robust generalization through a fresh perspective.
Unexpected Test Losses from Generalization Theory
Dive into generalization theory's impact on unexpected test losses, exploring emerging settings and their implications for machine learning models.
Generalization from the Behavioral Perspective
Dive into the behavioral perspective of generalization, exploring emerging settings and their implications in machine learning and data analysis.
Do Simpler Models Exist and How Can We Find Them? - Cynthia Rudin
Explore simpler, interpretable machine learning models and their advantages in criminal justice and beyond. Learn about the Rashomon set, decision trees, and strategies to combat overfitting.
What Do Our Models Learn? - Aleksander Mądry
Exploring machine learning model behavior, biases in datasets, and challenges in dataset creation and replication for more robust and reliable AI systems.
Brief Introduction to Deep Learning and the "Alchemy" Controversy - Sanjeev Arora
Explore deep learning's scientific foundations and the "Alchemy" debate, examining optimization, overfitting, and the interpreter problem in neural networks.
TensorFlow Tutorials 神经网络 教学 教程
从零开始学习TensorFlow构建神经网络，涵盖基础概念、CNN、RNN等高级主题，通过实例和可视化深入理解深度学习原理和应用。
Demystifying AI - Understanding Machine Learning Fundamentals
Fundierte Einführung in Künstliche Intelligenz: Grundlagen des maschinellen Lernens, mathematische Konzepte und Herausforderungen werden verständlich erklärt. Praktische Tipps für den Einstieg inklusive.
Regularization via Permutation of Labels - Lecture 1
Explore regularization techniques using label permutation in mathematical statistics, focusing on innovative approaches and their applications.
Institutions, Innovation, and the Social Production of Collective (Un)Intelligence
Explore how institutions and innovation shape collective intelligence, examining scientific attention, discovery limitations, and strategies for fostering creativity in research and knowledge production.
Do ImageNet Classifiers Generalize to ImageNet? - Paper Explained
Exploring ImageNet classifiers' generalization capabilities through new test datasets, revealing surprising accuracy drops and insights into model performance on slightly "harder" images.
Decision Trees - A Friendly Introduction
Learn to build and train decision trees through a simple example, exploring key concepts like data splitting, overfitting prevention, and alternative evaluation metrics.
Robust, Interpretable Statistical Models - Sparse Regression with the LASSO
Explore sparse regression and LASSO algorithm for building interpretable, robust models with minimal variables. Learn key concepts and applications in data science and machine learning.
The Elephant in your Dataset: Addressing Bias in Machine Learning
Explore bias in machine learning, its societal impact, and strategies to mitigate it. Learn to create fair and ethical AI systems through hands-on examples and cutting-edge research.