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Вернуться к Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Отзывы учащихся о курсе Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization от партнера

Оценки: 60,677
Рецензии: 7,028

О курсе

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....

Лучшие рецензии


13 янв. 2020 г.

After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.


18 апр. 2020 г.

Very good course to give you deep insight about how to enhance your algorithm and neural network and improve its accuracy. Also teaches you Tensorflow. Highly recommend especially after the 1st course

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326–350 из 6,976 отзывов о курсе Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

автор: Intan D Y

17 авг. 2018 г.

This course helps practitioner or beginner to know how to tune supporting parameters in order to achieve more efficient/accurate NN. In other words, this course helps me figuring how to optimize the NN design, and I think this is recommended for beginners who like to explore Deep Learning/NN

автор: Nachiket R A

14 апр. 2018 г.

This course provided a lot of insight in how to improve accuracy by tuning hyper parameters and also introduced multi-class problems and Deep learning programming frameworks! Awesome specialization to have as it aims to create well rounded expertise in Deep Learning and Neural networks area.

автор: Ekamjot S T

7 июля 2020 г.

After the first course, This course was really important for optimizing the Deep Nets and increasing its accuracy to further heights. The discussion forum also helped in clarification of many intriguing doubts. The assignments were suffice in implementing and understanding the fundamentals.

автор: Малышев Я

7 июня 2020 г.

Отличное продолжение первого курса от этой организации. Множество важных моментов отлично описаны, а задачи по программированию помогают закрепить это на практике. Как отдельный курс наверное не стоит наверное смотреть, но если рассматривать всю специализацию целиком - это отличный продукт!

автор: Vaibhav Y

12 дек. 2019 г.

Coursera is so amazing to provide an opportunity like this to someone who is living in a 3rd world country with almost no opportunities in a high entry barrier field like Data Science. It is inspiring to see what coursers stands for, providing a learning opportunity to everyone, everywhere.

автор: Sinan G

8 мая 2018 г.

Nice breadth and depth of relevant topics in this course. Andrew Ng is as always very precise about the issues presented and helps build up our knowledge step-by-step in a super structured way. Nice to work with both Python (semi-raw) models and getting a similar introduction to TensorFlow.

автор: Rajeev G

9 мая 2020 г.

Took the course to retest my knowledge in Deep learning. Have completed this course some time back. Without certificate. Professor has covered each of these topics in good detail. Practice workbooks and assignments are really helpful and provide a great start for deep learning enthusiasts.

автор: Xiang J

24 окт. 2019 г.

Really like the assignments in this course, which gives me hands-on experience with advanced knowledge such as Adam optimizer, gradient checking. Tensorflow v1 assignment is also good, but I am not sure whether API is still relevant as Keras based API for tensorflow v2 is already released.

автор: Tarush S

16 мая 2019 г.

With this course, even the beginner can understand why what happens when tuning and optimizing a neural network model. With easy to understand methodology and great explanation, I highly recommend this course for anyone who wants to go deeper into deep learning and understand the workings.

автор: Meghdad P

5 авг. 2018 г.

Very helpful learning material.

I'm still a bit confused though, even after passing the exams and exercises, but I think its mostly because I've lost grasp on mathematics. So, the blame is on me not coursera.

Hopefully I would fit more in the Deep Learning world by finishing up the course ;)

автор: Millard A C

9 февр. 2018 г.

This is a great course and you get to do real programming and training of a Deep Neural network. Andrew Ng is an excellent instructor. The final assignment wasn't hard but the syntax was difficult to follow. Using the forum and the Tensorflow documentation you can make your way through.

автор: Bill T

4 февр. 2018 г.

This builds on the basics from the first course with some important techniques (such as Xavier initialization, Adam optimization, and batchnorm) and ends with an introduction to implementing these in TensorFlow. Fast-moving but well taught with a good mix of theory and hands-on exercises.

автор: Yevhen D

24 июня 2020 г.

Awesome course. Theory and practise in the right proportion. Programming assignments are useful, interesting and use modern technologies like Python or TensorFlow. Question quizzes are not too hard but help to repeated theory. Also, I liked interviews with great people from Deep Learning.

автор: Sari T

25 июля 2019 г.

I am totally enlightened by this course. A lot of the concepts covered were completely new to me and very helpful in building a good performing neural network. The lectures were in depth and very well organized. The contents are not something you will come across in other tutorial sites.

автор: Bryan W

18 янв. 2018 г.

A great refresher to Andrew's original ML course at first, but also later is learning current deep learning current mindset at work. Great pace, great course, and great programming assignments. Makes me want to see the 3rd course for (i hope) more challenging programming assignments :) .

автор: harm l

3 сент. 2017 г.

Gave me a clear understanding on how to improve the calculus on a neural network. Computational software has advanced from programming in R of Python to software frameworks, hiding a lot of the math. Needs another study of the software frameworks though!

Thanks for the opportunity to join.

автор: Maryam

19 июня 2019 г.

prof. Ng's teaching was so great. some tricky details taught that I never considered them before. when I read the textbook, it was easy to understand and repetitive. I've learned simple and clean implementation. in overall it was important, simple, understandable, time efficient course.

автор: Rahul K

28 февр. 2018 г.

A very well structured course on some of the most overlooked (but critical) elements in Deep Learning. Prof. Andrew Ng definitely makes everything seem easy; he breaks down even the most complex of optimization algorithms and explains it with sheer simplicity. Would definitely recommend!

автор: Pranaya M

6 авг. 2018 г.

Course has been designed so well that even a aspiring beginner can learn the concepts very well.

Every student who wants to begin their career in the field of Deep Learning must follow this course.

Especially the tensor flow concept is taught very well with the help of exercise tutorial.

автор: David J

7 янв. 2018 г.

Thank you Andrew and Team for this course. I must say the course has surprised me and I have myself surprised my level of learning. But all credit to the way course is laid out and the step by step method of progress along with strong conceptual explanation helps a lot. Thank you again

автор: Farhodbek S

10 янв. 2021 г.

This specialization course gave me a better understanding of hyperparameters and the process of tuning them. Learning new information will help me build my own project without unexpected results. Andrew Ng still gives better intuition. I really appreciate the materials in this course.

автор: Thanh-Long N

14 окт. 2020 г.

An excellent course by Andrew about how to improve deep learning models. I actually thought about something over-emphasized before taking the course, but after completing it I have changed my mind completely: THIS COURSE IS A MUST IF YOU SERIOUSLY WANT TO GET INTO DEEP LEARNING WORLD!

автор: Rahul V

1 июня 2020 г.

Awesome Course! :)

Andrew is really the best instructor... He makes problems very easy to solve.

The content is fantastic...

The best part of this course is Optimization algorithms.

I loved every video and content with best explanation on hyperparameter tuning...

Adam optimization is best

автор: Lavkumar M

15 апр. 2020 г.

A great course, with deep understanding of all important hyperparameters and the related concepts important to tune the deep neural networks. Lectures are up to the mark and so are the programming assignments. Thanks a lot Andrew Ng and Coursera for making it possible for me to learn.

автор: Alejandro R V

2 янв. 2018 г.

As usual, another incredible course taught by a really good teacher. I strongly recommend it to anyone who wants to get a firm garsp about optimization algorithms and how they really work, apart from hyperparameter tunning and regularization methods for bias/variance. Thank Andrew Ng!