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

Оценки: 60,786
Рецензии: 7,040

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


4 апр. 2021 г.

Fantastic course and although it guides you through the course (and may feel less challenging to some) it provides all the building blocks for you to latter apply them to your own interesting project.


23 дек. 2017 г.

Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow\n\nThanks.

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401–425 из 6,992 отзывов о курсе Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

автор: Sagren P

4 сент. 2017 г.

This specialisation is an exciting journey - can't wait to start the next course. The foundational concepts of neural networks are expertly packaged in these courses, together with enough practical exposure to get you started on a fun learning and career experience.

автор: Akash K

8 авг. 2020 г.

Best course to improve your understanding of Neural Network tuning, moreover the Tensorflow course at the end of 3rd week is really detailed, I worked earlier with tensorflow but didnt get its details accurately, but now I am confident enough about using tensorflow

автор: Neil S

17 июня 2019 г.

Wonderful course that teaches one the intricacies of training better models. It's also great when learning to implement a neural network through Tensor Flow for the last assignment and realizing that you have a good understanding of whats going on "under the hood".

автор: Andrey M

9 апр. 2020 г.

This course is very thorough and detailed. Now I can clearly and confidently say that I can perform good research and obtain formal information and data on any topic, as opposed to just surfing the internet for genuine knowledge. Great course, well done to Andrew.

автор: Michael S

4 авг. 2018 г.

Overall, this is an excellent course, although it is not perfect. Trying to understand what is wrong when full credit is not earned for quizzes or programming assignments is sometimes "challenging". It would sometimes be useful to have more informative feedback.

автор: Ertu S

18 мая 2018 г.

Great course., excellent well to the point, Only nuisance I observed is during submitting coding assingments required multiple tries since at first time, all the code somehow does not go thru. So needed to save and restart notebook and cut& pasted again. Thank you

автор: Белоусов А Ю

23 сент. 2017 г.

Great course. I really like it as it get more and more practical.

Few things might be missing from the class - it might be worth to encourage students play with algorithms a bit more. Say get back to the previous stage and add regularization to get better results.

автор: Christos Z

30 апр. 2018 г.

Grate course, only criticism is that week 3 didn't thoroughly explain how batch normalization parameters (gamma and beta) get updated during gradient descent. (i.e. how to get dgamma and dbeta). It could have been an optional lecture for the mathematically savvy)

автор: Siddhant A

23 сент. 2020 г.

One of the most comprehensive course for people who want to learn the logic, implementation and conceptual understanding for deep learning. Programming exercises are a great starting point for learning implementation and they are perfectly made for the learners.

автор: Abdelrahman A

19 мая 2019 г.

it is wonderful course i learned more in Deep learning and how to apply regularization

and how to optimize cost function also programming in Tensor flow

i thanks all teaching assistant for there efforts to learn us

and i recommend this course to DL beginners

автор: manish m

28 апр. 2018 г.

I recommend everyone to go through this course if you really want to learn detail about hyperparameter tuning , optimizers and regularization used to make neural network better. It helps to open black box of Neural network and know in detail about how all works.

автор: Lee F

5 сент. 2017 г.

Some very useful insights into practical implementation and optimization of neural networks, and a very welcome introduction to TensorFlow. After coding networks in numpy you both appreciate the framework, as well as understand what it's doing behind the scenes.

автор: Sebastian E G

18 авг. 2017 г.

Again, fantastic. Great way to explain how to tune your algorithms to improve bias and variance. Great explanation of what optimizers are used and how they function. Glad to know the nuts and bolts of the parameters usually defined in machine learning frameworks

автор: Yizhe

31 мая 2021 г.

This cause is good for me. It taught me how to tune hyperparameters and correct regularisation and optimisation to speed up the process of Machine Learning. I got some useful knowledge to utilise Tensorflow to quickly create a model and put it into the product.

автор: Harsh B

6 нояб. 2017 г.

This course is a must for understanding hyperparameters and their tuning and choosing the best ones for your model. Prof. Andrew explains everything very simply and precisely. This course is intended for intermediate users who have knowledge with Deep networks.

автор: Aman D

8 сент. 2017 г.

I think the most important course of the 1st 3. It tells about all the different optimizations and practical aspects of training a deep neural network. I would keep referring to its content in the future too. Thank you team for creating such a wonderful course.

автор: kunal s

14 авг. 2017 г.

This was one the best course as it has made me capable to increase the efficiency of a project as it has taught me various techniques of selection of data size ratios, tunning hyper-parameters speeding gradient checking using different techniques and many more.

автор: Sampath T

23 сент. 2021 г.

I would like to thank coursera to giving me opportunity to follow Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization course. In the last few weeks I learned a lot of new theories and basics of improving deep neural networks.

автор: Juan P M M

10 февр. 2021 г.

Este curso ofrece una información más interesante que el primero, teniendo en cuenta que ya se saben los conceptos básicos. Hay un contenido de calidad que está bien explicado, sobre todo ayuda de cara a saber por qué se eligen los hiper parámetros en una red.

автор: Beltus N

1 июня 2020 г.

The gentle transition from NumPy based implemented deep learning functions to the Google's TensorFlow framework is so smooth and easy to comprehend. My understanding of the concepts has been solidified by the course. Thank you Andrew Ng and the Coursera team.

автор: Tú A N

28 окт. 2017 г.

Extremely useful course . I highly recommend it . This course give me some helpful tips to tune hyperparameters , some optimization techniques that never heard before . The intro to Tensorflow in third weed is great . Assignment also proves to be insightful .

автор: Dagart A

21 июня 2022 г.

V​ery in-depth course to understand how to fine tune hyperparameters properly and which hold the greatest significance for performance. The Regularization techniques are also interesting to learn what the theory behind Adam, RMSprop, and Momentum optimizers.

автор: Ka W P N

7 апр. 2019 г.

The course materials are well-designed. However, I have to say this is not an easy course as I spent a lot of efforts in order to understand how to do the assignments. Overall, I strongly believe the course has taught me what I need to know about this topic!

автор: helenhu

29 янв. 2021 г.

I've learn a lot from professor Andrew Ng.He is definitely my super idol.Thanks a lot.It‘s a pretty awesome platform for me to learn from the giant.From the course of machine-learning to deep-learning,I really feel like I've made a lot of progress.Thanks.

автор: Joshua D

18 мая 2020 г.

This was an interesting and challenging course. Andrew gives good intuitions about the fundamentals of improving deep neural networks. I recommend having separate optional sections explaining the math behind some of the concepts for those who are interested