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Federated Learning

Federated Learning Using PyTorch
5.0
5.0/5
(3 reviews)
30 students
Created by Mohamed Gharibi

9.8

CourseMarks Score®

10.0

Freshness

9.6

Feedback

9.2

Content

Platform: Udemy
Price: $11.99
Video: 7h 56m
Language: English
Next start: On Demand

Top PyTorch courses:

Detailed Analysis

CourseMarks Score®

9.8 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

10.0 / 10
This course was last updated on 4/2021.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

9.6 / 10
We analyzed factors such as the rating (5.0/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

9.2 / 10
Video Score: 8.8 / 10
The course includes 7h 56m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 4 hours 37 minutes of 15 PyTorch courses on Udemy.
Detail Score: 9.3 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

4 articles.
0 resource.
0 exercise.
0 test.

Table of contents

Description

The course starts by introducing you to the main concepts in Neural Networks (NN) and how do they work. Then we will implement a NN from scratch using Pytorch. After that, a quick introduction to Federated Learning architecture. Then, we will start by loading the dataset on the devices in IID, non-IID, and non-IID and unbalanced settings followed by a quick tutorial on PySyft to show you how to send and receive the models and the datasets between the clients and the server.
This course will teach you Federated Learning (FL) by looking at the original papers’ techniques and algorithms then implement them line by line. In particular, we will implement FedAvg, FedSGD, FedProx, and FedDANE. You will learn about Differential Privacy (DP) and how to add it to FL, then we will implement FedAvg using DP. In this course, you will learn how to implement FL techniques locally and on the cloud.  For the cloud setting, we will use Google Cloud Platform to create and configure all the instances that we will use in our experiments. By the end of this course, you will be able to implement different FL techniques and even build your own optimizer and technique. You will be able to run your experiments locally and on the cloud.

Requirements

• Python Programming Language

You will learn

✓ Introduction to Deep Learning and Neural Networks
✓ Introduction to Federated Learning
✓ Build Neural Networks from scratch using PyTorch
✓ Load your datasets in IID, non-IID, and non-IID unbalanced settings
✓ Introduction to PySyft
✓ Federated Learning techniques (FedAvg, FedSGD, FedProx, FedDANE)
✓ Build your custom optimizer using PyTorch
✓ Introduction to Differential Privacy
✓ Implement FedAvg using Differential Privacy
✓ Federated Learning on cloud
✓ Implement FedAvg on cloud

This course is for

• Federated Learning enthusiasts

How much does the Federated Learning course cost? Is it worth it?

The course costs $11.99. And currently there is a 87% discount on the original price of the course, which was $94.99. So you save $83 if you enroll the course now.
The average price is $17.3 of 15 PyTorch courses. So this course is 31% cheaper than the average PyTorch course on Udemy.

Does the Federated Learning course have a money back guarantee or refund policy?

YES, Federated Learning has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Federated Learning course, but there is a $83 discount from the original price ($94.99). So the current price is just $11.99.

Who is the instructor? Is Mohamed Gharibi a SCAM or a TRUSTED instructor?

Mohamed Gharibi has created 1 courses that got 3 reviews which are generally positive. Mohamed Gharibi has taught 30 students and received a 5.0 average review out of 3 reviews. Depending on the information available, Mohamed Gharibi is a TRUSTED instructor.

More info about the instructor, Mohamed Gharibi

Federated Learning
Ph.D. in Computer Science, Machine Learning enthusiast. My expertise falls under the intersection of software engineering and machine learning. I like to blog and teach about Deep Learning and Federated Learning. I am a certified Tensorflow programmer by Google.  I was funded as a Research Assistant under the IUCRC-National Science Foundation (NSF).

9.8

CourseMarks Score®

10.0

Freshness

9.6

Feedback

9.2

Content

Platform: Udemy
Price: $11.99
Video: 7h 56m
Language: English
Next start: On Demand

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