RR
4.0Reviewed Jun 12, 2020
Could have increased assignments and some more indepth knowledge of tensorflow and proper installation way of tensorflow cause mine is showing error when iam trying to practice as shown in the video
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 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.
Status: Model Evaluation
Status: Model Training
IntermediateCourse24 hours
Featured reviews
AA
4.0Reviewed Oct 22, 2017
Assignment in week 2 could not tell the difference between 'a-=b' and 'a=a-b' and marked the former as incorrect even though they are the same and gave the same output. Other than that, a great course
AS
5.0Reviewed Nov 19, 2018
This course is a big part of the meat of the Deep Learning specialization. I found both lectures and exercises gave me valuable practice at grappling with the actual process of training neural nets.
SC
5.0Reviewed Feb 14, 2018
A valuable course in enhancing one's ability to properly identify the correct Hyperparameter to tune according to the situation - a critical task in day-to-day debugging & tuning of an algorithm.
DD
5.0Reviewed Mar 28, 2020
I have done two courses under Andrew ng and I am grateful to Coursera for their highly optimised and easily learning course structure. It has greatly help me gain confidence in this field. Thank you.
NT
4.0Reviewed Aug 19, 2019
I think this course is great. Because we learn about some definitions about hyperparameters, optimization which are frequently appears in papers or in the functions in some Deep Learning frameworks.
NC
5.0Reviewed Jun 2, 2018
Just as great as the previous course. I feel like I have a much better chance at figuring out what to do to improve the performance of a neural network and TensorFlow makes much more sense to me now.
NC
5.0Reviewed Aug 18, 2017
Yet another excellent course by Professor Ng! Really helped me gain a detailed understanding of optimization techniques such as RMSprop and Adam, as well as the inner workings of batch normalization.
BA
4.0Reviewed May 31, 2020
Very good course, useful and smart. Some of the example are on tensorflow 1 but I think that they will update them soon to keras tf2 Thank you!I will pass on what I have learned here to undergrads :)
HJ
4.0Reviewed Jun 10, 2020
great and practical insight. carefully crafted assignments. still coding in python and the quirks coming with it are sometimes of equal difficulty if not worse than understanding the explained theory
HK
5.0Reviewed Aug 7, 2021
As a beginner who learn machine learning for 2 months, this course guide me to the basic concepts of hyperparameter tuning! I think I can come back to here while I practice machine learning projects!
DH
5.0Reviewed Apr 26, 2020
Everything, Everyparameter in neural networks looks familiar to me now. I feel like I can optimize them for better accuracy. Overall I learned some new things and the way of teaching was really nice.