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Udemy - Optimizers in Machine Learning and Deep Learning



Size :1.0 GB
Peers : Seeders : 0      Leechers : 0
Added : 5 hours ago » by freecoursewb » in Other
Language : English
Last Updated :5 hours ago
Info_Hash :76FE3D38FB169190423408FBAAB0A49D0F206134

Torrent File Contents

Udemy - Optimizers in Machine Learning and Deep Learning
  Get Bonus Downloads Here.url
  -  182 Bytes

  ~Get Your Files Here !/1 - Introduction/1 - Introduction.mp4
  -  14.43 MB

  ~Get Your Files Here !/2 - Stochastic Gradient Descent/1 - Stochastic Gradient Descent (SGD) - Intro.mp4
  -  37.04 MB

  ~Get Your Files Here !/2 - Stochastic Gradient Descent/2 - SGD with Mean Squared Error - Gradient derivation.mp4
  -  23.23 MB

  ~Get Your Files Here !/2 - Stochastic Gradient Descent/3 - SGD - Excel implementation.mp4
  -  43.72 MB

  ~Get Your Files Here !/2 - Stochastic Gradient Descent/4 - SGD - Validating excel outputs using TensorFlow.mp4
  -  33.64 MB

  ~Get Your Files Here !/2 - Stochastic Gradient Descent/5 - SGD - Pros and Cons.mp4
  -  28.99 MB

  ~Get Your Files Here !/3 - Momentum/1 - Momentum - Intro.mp4
  -  6.92 MB

  ~Get Your Files Here !/3 - Momentum/2 - Momentum - Excel implementation.mp4
  -  73.59 MB

  ~Get Your Files Here !/3 - Momentum/3 - Momentum - Validating excel outputs using TensorFlow.mp4
  -  31.71 MB

  ~Get Your Files Here !/3 - Momentum/4 - Momentum - Pros and Cons.mp4
  -  5.76 MB

  ~Get Your Files Here !/4 - NAG/1 - NAG - Intro.mp4
  -  9.44 MB

  ~Get Your Files Here !/4 - NAG/2 - NAG - Excel implementation.mp4
  -  66.69 MB

  ~Get Your Files Here !/4 - NAG/3 - NAG - Validating excel outputs using TensorFlow.mp4
  -  25.41 MB

  ~Get Your Files Here !/4 - NAG/4 - NAG - Pros and Cons.mp4
  -  7.99 MB

  ~Get Your Files Here !/5 - Adagrad/1 - Adagrad - Intro.mp4
  -  80.4 MB

  ~Get Your Files Here !/5 - Adagrad/2 - Adagrad - Excel implementation.mp4
  -  102.46 MB

  ~Get Your Files Here !/5 - Adagrad/3 - Adagrad - Validating excel outputs using TensorFlow.mp4
  -  36.29 MB

  ~Get Your Files Here !/5 - Adagrad/4 - Adagrad - Pros and Cons.mp4
  -  12.5 MB

  ~Get Your Files Here !/6 - RMSprop/1 - RMSprop - Intro.mp4
  -  14.9 MB

  ~Get Your Files Here !/6 - RMSprop/2 - RMSprop - Excel implementation.mp4
  -  19.67 MB

  ~Get Your Files Here !/6 - RMSprop/3 - RMSprop - Validating excel outputs using TensorFlow.mp4
  -  18.5 MB

  ~Get Your Files Here !/6 - RMSprop/4 - RMSprop - Pros and Cons.mp4
  -  5.19 MB

  ~Get Your Files Here !/7 - Adam/1 - Adam - Intro.mp4
  -  21.99 MB

  ~Get Your Files Here !/7 - Adam/2 - Adam - Excel implementation.mp4
  -  56.29 MB

  ~Get Your Files Here !/7 - Adam/3 - Adam - Validating excel outputs using TensorFlow.mp4
  -  29.37 MB

  ~Get Your Files Here !/7 - Adam/4 - Adam - Pros and Cons.mp4
  -  13.9 MB

  ~Get Your Files Here !/8 - Gradient derivation for different loss and activation functions/1 - Gradient derivation - Intro.mp4
  -  17.19 MB

  ~Get Your Files Here !/8 - Gradient derivation for different loss and activation functions/2 - SGD with Mean Absolute Error.mp4
  -  21.25 MB

  ~Get Your Files Here !/8 - Gradient derivation for different loss and activation functions/3 - SGD with Root Mean Squared Error.mp4
  -  25.1 MB

  ~Get Your Files Here !/8 - Gradient derivation for different loss and activation functions/4 - SGD with ReLu Activation and Mean Absolute Error.mp4
  -  70.23 MB

  ~Get Your Files Here !/8 - Gradient derivation for different loss and activation functions/5 - SGD with Sigmoid Activation and Binary Log loss - Part 1.mp4
  -  77.35 MB

  ~Get Your Files Here !/8 - Gradient derivation for different loss and activation functions/6 - SGD with Sigmoid Activation and Binary Log loss - Part 2.mp4
  -  17.01 MB

  ~Get Your Files Here !/8 - Gradient derivation for different loss and activation functions/7 - Summary of gradients.mp4
  -  3.04 MB

  ~Get Your Files Here !/Bonus Resources.txt
  -  386 Bytes



Torrent 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.