Udemy - Optimizers in Machine Learning and Deep Learning

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[ DevCourseWeb.com ] Udemy - Optimizers in Machine Learning and Deep Learning
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction
    • 1 - Introduction.mp4 (14.4 MB)
    2 - Stochastic Gradient Descent
    • 1 - Stochastic Gradient Descent (SGD) - Intro.mp4 (37.0 MB)
    • 2 - SGD with Mean Squared Error - Gradient derivation.mp4 (23.2 MB)
    • 3 - SGD - Excel implementation.mp4 (43.7 MB)
    • 4 - SGD - Validating excel outputs using TensorFlow.mp4 (33.6 MB)
    • 5 - SGD - Pros and Cons.mp4 (29.0 MB)
    3 - Momentum
    • 1 - Momentum - Intro.mp4 (6.9 MB)
    • 2 - Momentum - Excel implementation.mp4 (73.6 MB)
    • 3 - Momentum - Validating excel outputs using TensorFlow.mp4 (31.7 MB)
    • 4 - Momentum - Pros and Cons.mp4 (5.8 MB)
    4 - NAG
    • 1 - NAG - Intro.mp4 (9.4 MB)
    • 2 - NAG - Excel implementation.mp4 (66.7 MB)
    • 3 - NAG - Validating excel outputs using TensorFlow.mp4 (25.4 MB)
    • 4 - NAG - Pros and Cons.mp4 (8.0 MB)
    5 - Adagrad
    • 1 - Adagrad - Intro.mp4 (80.4 MB)
    • 2 - Adagrad - Excel implementation.mp4 (102.5 MB)
    • 3 - Adagrad - Validating excel outputs using TensorFlow.mp4 (36.3 MB)
    • 4 - Adagrad - Pros and Cons.mp4 (12.5 MB)
    6 - RMSprop
    • 1 - RMSprop - Intro.mp4 (14.9 MB)
    • 2 - RMSprop - Excel implementation.mp4 (19.7 MB)
    • 3 - RMSprop - Validating excel outputs using TensorFlow.mp4 (18.5 MB)
    • 4 - RMSprop - Pros and Cons.mp4 (5.2 MB)
    7 - Adam
    • 1 - Adam - Intro.mp4 (22.0 MB)
    • 2 - Adam - Excel implementation.mp4 (56.3 MB)
    • 3 - Adam - Validating excel outputs using TensorFlow.mp4 (29.4 MB)
    • 4 - Adam - Pros and Cons.mp4 (13.9 MB)
    8 - Gradient derivation for different loss and activation functions
    • 1 - Gradient derivation - Intro.mp4 (17.2 MB)
    • 2 - SGD with Mean Absolute Error.mp4 (21.3 MB)
    • 3 - SGD with Root Mean Squared Error.mp4 (25.1 MB)
    • 4 - SGD with ReLu Activation and Mean Absolute Error.mp4 (70.2 MB)
    • 5 - SGD with Sigmoid Activation and Binary Log loss - Part 1.mp4 (77.4 MB)
    • 6 - SGD with Sigmoid Activation and Binary Log loss - Part 2.mp4 (17.0 MB)
    • 7 - Summary of gradients.mp4 (3.0 MB)
    • Bonus Resources.txt (0.4 KB)

Description

Optimizers in Machine Learning and Deep Learning

https://DevCourseWeb.com

Published 8/2024
Created by Mac Data Insights
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 34 Lectures ( 2h 5m ) | Size: 1 GB

A deep dive into the math behind popular optimizers in machine learning and deep learning

What you'll learn:
Understand the math behind popular optimizers - Stochastic gradient descent, Momentum, NAG, Adagrad, RMSprop, Adam
Gain intuition behind each of these optimizers, so you can decide the best optimizer for a given dataset
Revise TensorFlow basics
Master hyperparameter tuning of each of these optimizers in TensorFlow
Perform optimization calculations by hand and match the results with the outputs generated by TensorFlow optimizer libraries

Requirements:
A basic understanding of machine learning and the role of optimizers is beneficial.



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Udemy - Optimizers in Machine Learning and Deep Learning

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1 GB
seeders:2
leechers:15
Udemy - Optimizers in Machine Learning and Deep Learning

Torrent hash: 76FE3D38FB169190423408FBAAB0A49D0F206134