NeurASP: Experiments and Evaluation - Part 3
Explore experimental evaluations of NeurASP, a neuro-symbolic framework connecting ASP programs to neural architectures, enabling end-to-end training through gradient passage.
Logistic Regression and the Perceptron Algorithm - A Friendly Introduction
Friendly introduction to logistic regression and perceptron algorithm, covering key concepts like data classification, gradient descent, and neural networks with minimal math and visual explanations.
A Friendly Introduction to Convolutional Neural Networks and Image Recognition
Friendly explanation of image recognition using Convolutional Neural Networks, covering basics to advanced concepts. Suitable for beginners, with minimal math required.
A Friendly Introduction to Deep Learning and Neural Networks
Explore neural networks and deep learning concepts, from gradient descent to activation functions, in this friendly introduction to AI fundamentals.
A Friendly Introduction to Machine Learning
Explore key machine learning algorithms through clear examples, from linear regression to neural networks and clustering techniques, in this beginner-friendly introduction.
MIT Introduction to Deep Learning
Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques for building effective models.
Introduction to Deep Learning
Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques for building effective models.
Machine Learning - The Bare Math Behind Libraries
Explore the mathematical foundations of machine learning, from basic algorithms to neural networks. Gain insights to confidently select and optimize ML models for practical applications.
Quantum Dynamics for Continuous Optimization - Quantum Hamiltonian Descent
Explore quantum Hamiltonian descent algorithms for solving complex optimization problems, focusing on practical applications and implementation through the QHDOPT software package.
Non-convex Matrix Sensing: Breaking the Quadratic Rank Barrier in Sample Complexity
Explore groundbreaking research on non-convex matrix sensing, focusing on improved algorithms for reconstructing low-rank matrices using fewer samples through innovative probabilistic decoupling methods.
PyTorch Tutorial: Deep Learning Framework Fundamentals - Fall 2022
Master PyTorch fundamentals and deep learning concepts, from tensor operations to GPU acceleration, autograd, and data loading - all through hands-on coding examples and practical implementations.
Machine Learning: Least Mean Square Regression - Lecture 12
Dive into advanced linear regression concepts, exploring overfitting, gradient descent, and practical plotting techniques for enhanced machine learning understanding.
Haskell for Choice-Based Learning in Machine Learning Programming
Explore language design for machine learning, focusing on choice-based learning. Combine algebraic effects, handlers, and selection monad to create powerful frameworks for decision-making models and optimization techniques.
Multi-Precision Optimization Algorithm: Decreasing Computational Cost and Controlling Computational Error
Explore multi-precision optimization algorithms to reduce computational costs and control errors in smooth, non-convex function minimization, with applications in deep neural network training.
EfficientML.ai - Basics of Neural Networks - Lecture 2
Explore neural network fundamentals, including architecture, training, and optimization techniques. Gain insights into efficient machine learning concepts and applications.
Basics of Neural Networks - EfficientML.ai Lecture 2
Explore neural network fundamentals, including architecture, training, and optimization techniques, in this comprehensive MIT lecture on efficient machine learning.
Random Initialization and Implicit Regularization in Nonconvex Statistical Estimation - Lecture 2
Explore nonconvex optimization in statistical estimation, focusing on gradient descent's convergence to optimal solutions without explicit regularization or careful initialization.
Early Alignment in Two-Layer Networks Training: A Two-Edged Sword
Explore early alignment in two-layer network training, examining its dual nature as both beneficial and potentially problematic for neural network performance.
Methods of Optimization in Machine Learning
Explore optimization techniques for machine learning models, including gradient descent and stochastic methods. Learn to adjust hyperparameters and minimize cost functions effectively.
Computer-Aided Lyapunov Analyses and Counter-Examples to the Convergence of First-Order Optimization Methods
Explore computer-aided Lyapunov analyses and counter-examples for first-order optimization methods, focusing on constructive approaches and structural properties.
Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter
Explore differentially private stochastic optimization techniques for loss functions with large worst-case Lipschitz parameters, addressing outliers and heavy-tailed data for improved excess risk bounds.