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Optimizers in Machine Learning and Deep Learning --> [ DevCourseWeb ]

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English

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1.03 GB

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76fe3d38fb169190423408fbaab0a49d0f206134

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09-09-2024 17:36

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File	Size
Get Bonus Downloads Here.url	182.00 B
~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	80.40 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	12.50 MB
~Get Your Files Here !/6 - RMSprop/1 - RMSprop - Intro.mp4	14.90 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	18.50 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	13.90 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	25.10 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	386.00 B

Description

[ DevCourseWeb.com ] Optimizers in Machine Learning and Deep Learning

If You Need More Stuff, kindly Visit and Support Us -->> 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.

If You Need More Stuff, kindly Visit and Support Us -->> https://CourseWikia.com

Get More Tutorials and Support Us -->> https://FreeCourseWeb.com

We upload these learning materials for the people from all over the world, who have the talent and motivation to sharpen their skills/ knowledge but do not have the financial support to afford the materials. If you like this content and if you are truly in a position that you can actually buy the materials, then Please, we repeat, Please, Support Authors. They Deserve it! Because always remember, without "Them", you and we won't be here having this conversation. Think about it! Peace...