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Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and ge...
4.9
4.9/5
(58,813 reviews)
406,107 students
Created by

9.5

CourseMarks Score®

N/A

Freshness

9.7

Feedback

8.8

Content

Platform: Coursera
Video: 5h 26m
Language: English

Top Machine Learning courses:

Detailed Analysis

CourseMarks Score®

9.5 / 10

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

Freshness Score

Course content can become outdated quite quickly.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

9.7 / 10
We analyzed factors such as the rating (4.9/5) 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

8.8 / 10
Video Score: 8.4 / 10
The course includes 5h 26m 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 5 hours 48 minutes of 749 Machine Learning courses on Coursera.
Detail Score: 8.0 / 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: 9.8 / 10

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

This course contains:

15 articles.
0 resource.
0 exercise.
8 tests or quizzes.

Table of contents

Description

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.

By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow.

The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

You will learn

Requirements

Basic knowledge of Machine Learning is required to start this course, as this is an intermediate level course.

This course is for

This course was made for intermediate-level students.

How much does the Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization course cost? Is it worth it?

The course costs $0.
The average price is $13.6 of 749 Machine Learning courses. So this course is 100% cheaper than the average Machine Learning course on Coursera.

Does the Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization course have a money back guarantee or refund policy?

Coursera offers a 7-day free trial for subscribers.

Are there any SCHOLARSHIPS for this course?

YES, you can get a scholarship or Financial Aid for Coursera courses. The first step is to fill out an application about your educational background, career goals, and financial circumstances. Learn more about financial aid on Coursera.

Who is the instructor? Is Andrew Ng a SCAM or a TRUSTED instructor?

Andrew Ng has created 31 courses that got 3,720 reviews which are generally positive. Andrew Ng has taught 5213957 students and received a 4.92 average review out of 3,720 reviews. Depending on the information available, Andrew Ng is a TRUSTED instructor.
Founder, DeepLearning.AI & Co-founder, Coursera
DeepLearning.AI
Andrew Ng is Founder of DeepLearning.AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University. As a pioneer both in machine learning and online education, Dr. Ng has changed countless lives through his work in AI, authoring or co-authoring over 100 research papers in machine learning, robotics, and related fields. Previously, he was chief scientist at Baidu, the founding lead of the Google Brain team, and the co-founder of Coursera – the world’s largest MOOC platform. Dr. Ng now focuses his time primarily on his entrepreneurial ventures, looking for the best ways to accelerate responsible AI practices in the larger global economy.

9.5

CourseMarks Score®

N/A

Freshness

9.7

Feedback

8.8

Content

Platform: Coursera
Video: 5h 26m
Language: English

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