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Gradient descent
Training Autoencoders by Alternating Minimization
Learning to Learn by Gradient Descent by Gradient Descent
Training Neural Networks Without Gradients: a Scalable ADMM Approach
Training Deep Networks with Stochastic Gradient Normalized by Layerwise Adaptive Second Moments
CSE 152: Computer Vision Manmohan Chandraker
Automated Source Code Generation and Auto-Completion Using Deep Learning: Comparing and Discussing Current Language Model-Related Approaches
GEE: a Gradient-Based Explainable Variational Autoencoder for Network Anomaly Detection
Optimization and Gradient Descent INFO-4604, Applied Machine Learning University of Colorado Boulder
Gradient Descent (PDF)
Lecture 10: Recurrent Neural Networks
Stochastic Gradient Descent in Machine Learning
Lecture 6: Stochastic Gradient Descent Sanjeev Arora Elad Hazan
An Evolutionary Method for Training Autoencoders for Deep Learning Networks
Recurrent Neural Network
Section 3: Gradient Descent & Backpropagation Practice Problems
Lecture 8: Optimization
Scaling Distributed Training with Adaptive Summation
Learning to Rank Using Gradient Descent
Top View
Sparse Autoencoder
Stochastic Gradient Descent Learning and the Backpropagation Algorithm
Gradient Origin Networks
Stochastic Gradient Descent As Approximate Bayesian Inference
Autoencoder-15-Mar-17.Pdf
Artificial Neural Networks 2Nd February 2017, Aravindh Mahendran, Student D.Phil in Engineering Science, University of Oxford
CSC321 Lecture 6: Backpropagation
Learning Recurrent Neural Networks with Hessian-Free Optimization
Introduction to Reinforcement Learning
Implicit Bias of Gradient Descent on Linear Convolutional Networks
THOR: Trace-Based Hardware-Driven Layer-Oriented Natural Gradient
Machine Learning Basics Lecture 3: Perceptron Princeton University COS 495 Instructor: Yingyu Liang Perceptron Overview
1 Lecture 10: Descent Methods Gradient Descent (Reminder)
Analysis of Standard Gradient Descent with GD Momentum and Adaptive LR for SPR Prediction
Lecture 7 – Deep Learning and Convolutional Networks ESS2222
On Orthogonality and Learning Recurrent Networks with Long Term Dependencies
A Survey of Optimization Methods from a Machine Learning Perspective
Calibrated Stochastic Gradient Descent for Convolutional Neural