Scanned by

    Norton

    Norton™ Safe Web

    Seminar on Generative Adversarial Network at DAIICT 2020

    Seminar on Generative Adversarial Network at DAIICT 2020

    01:17:27 |
    Download Here

    You Might Also Like:

    Lecture 1: Tensorflow Constants and Variables Part 1

    Lecture 1: Tensorflow Constants and Variables Part 1

    29:21 |
    Lecture 5: Gradient Tape Continued: Higher Order Derivatives

    Lecture 5: Gradient Tape Continued: Higher Order Derivatives

    10:39 |
    Lecture 15: Support Vector Machine 3 (Soft SVM and Kernel SVM)

    Lecture 15: Support Vector Machine 3 (Soft SVM and Kernel SVM)

    56:51 |
    Deep Learning 38: (1) Introduction to Adversarial AutoEncoders

    Deep Learning 38: (1) Introduction to Adversarial AutoEncoders

    18:34 |
    Deep Learning 27: (1) Generative Adversarial Network (GAN): Introduction and Back-Propagation

    Deep Learning 27: (1) Generative Adversarial Network (GAN): Introduction and Back-Propagation

    19:18 |
    Deep Learning 35: (2) Wasserstein Generative Adversarial Network (WGAN): Wasserstein metric

    Deep Learning 35: (2) Wasserstein Generative Adversarial Network (WGAN): Wasserstein metric

    29:46 |
    Ahlad kumar

    Ahlad kumar

    0:16 |
    Deep Learning 34: (1) Wasserstein Generative Adversarial Network (WGAN): Introduction

    Deep Learning 34: (1) Wasserstein Generative Adversarial Network (WGAN): Introduction

    31:03 |
    Lecture 9: Classification/ Logistic Regression

    Lecture 9: Classification/ Logistic Regression

    1:05:35 |
    Deep Learning 62: Introduction to Recurrent Neural Network (RNN)

    Deep Learning 62: Introduction to Recurrent Neural Network (RNN)

    17:34 |
    Deep Learning 60: Architecture of Graph Neural Network

    Deep Learning 60: Architecture of Graph Neural Network

    25:53 |
    Deep Learning 59: Fundamentals of Graph Neural Network

    Deep Learning 59: Fundamentals of Graph Neural Network

    17:05 |
    Deep Learning 61: Feed-forward Propagation in Graph Neural Network

    Deep Learning 61: Feed-forward Propagation in Graph Neural Network

    31:28 |
    Deep Learning 21: (3) Variational AutoEncoder : Working details of Variational AutoEncoder

    Deep Learning 21: (3) Variational AutoEncoder : Working details of Variational AutoEncoder

    17:55 |
    Deep Learning 63: (Part A) Architecture of Recurrent Neural Network (RNN)

    Deep Learning 63: (Part A) Architecture of Recurrent Neural Network (RNN)

    12:14 |
    Deep Learning 20: (2) Variational AutoEncoder : Explaining KL (Kullback-Leibler) Divergence

    Deep Learning 20: (2) Variational AutoEncoder : Explaining KL (Kullback-Leibler) Divergence

    16:52 |
    Lecture 6: Ridge Regression

    Lecture 6: Ridge Regression

    1:07:14 |
    Deep Learning 19: (1) Variational AutoEncoder : Introduction and Probability Refresher

    Deep Learning 19: (1) Variational AutoEncoder : Introduction and Probability Refresher

    18:34 |
    Lecture 7: Role of Regularization in Regression

    Lecture 7: Role of Regularization in Regression

    50:55 |
    Reinforcement Learning 5: Markov Property and State Transition Matrix

    Reinforcement Learning 5: Markov Property and State Transition Matrix

    19:50 |