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Gradient descent

  • Training Autoencoders by Alternating Minimization

    Training Autoencoders by Alternating Minimization

  • Learning to Learn by Gradient Descent by Gradient Descent

    Learning to Learn by Gradient Descent by Gradient Descent

  • Training Neural Networks Without Gradients: a Scalable ADMM Approach

    Training Neural Networks Without Gradients: a Scalable ADMM Approach

  • Training Deep Networks with Stochastic Gradient Normalized by Layerwise Adaptive Second Moments

    Training Deep Networks with Stochastic Gradient Normalized by Layerwise Adaptive Second Moments

  • CSE 152: Computer Vision Manmohan Chandraker

    CSE 152: Computer Vision Manmohan Chandraker

  • Automated Source Code Generation and Auto-Completion Using Deep Learning: Comparing and Discussing Current Language Model-Related Approaches

    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

    GEE: a Gradient-Based Explainable Variational Autoencoder for Network Anomaly Detection

  • Optimization and Gradient Descent INFO-4604, Applied Machine Learning University of Colorado Boulder

    Optimization and Gradient Descent INFO-4604, Applied Machine Learning University of Colorado Boulder

  • Gradient Descent (PDF)

    Gradient Descent (PDF)

  • Lecture 10: Recurrent Neural Networks

    Lecture 10: Recurrent Neural Networks

  • Stochastic Gradient Descent in Machine Learning

    Stochastic Gradient Descent in Machine Learning

  • Lecture 6: Stochastic Gradient Descent Sanjeev Arora Elad Hazan

    Lecture 6: Stochastic Gradient Descent Sanjeev Arora Elad Hazan

  • An Evolutionary Method for Training Autoencoders for Deep Learning Networks

    An Evolutionary Method for Training Autoencoders for Deep Learning Networks

  • Recurrent Neural Network

    Recurrent Neural Network

  • Section 3: Gradient Descent & Backpropagation Practice Problems

    Section 3: Gradient Descent & Backpropagation Practice Problems

  • Lecture 8: Optimization

    Lecture 8: Optimization

  • Scaling Distributed Training with Adaptive Summation

    Scaling Distributed Training with Adaptive Summation

  • Learning to Rank Using Gradient Descent

    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


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