Udemy - Optimizers in Machine Learning and Deep Learning
- Category Other
- Type Tutorials
- Language English
- Total size 1.0 GB
- Uploaded By freecoursewb
- Downloads 250
- Last checked 10 hours ago
- Date uploaded 1 year ago
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Infohash : 76FE3D38FB169190423408FBAAB0A49D0F206134
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.
Files:
[ 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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)
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