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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: 60,969

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.


26 авг. 2021 г.

Amazing course which focus on the theoretical part of parameters tuning, but it needs more explanation of Tensorflow, as I felt a little lost in the last project. Except that, it is an amazing course.

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476–500 из 7,026 отзывов о курсе Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

автор: Sy T

3 мая 2020 г.

Understand well the fundamental and most of the hyper-parameters tuning mechanism and processes. The course has included some fundamental knowledge of using TensorFlow. It is well organized in the teaching processes and programming assignment.

автор: Teye B

27 мар. 2018 г.

Great Course. I especially like how the mathematics of Deep NNs are presented, taught and practiced, before the revelation of frameworks such as Tensorflow(in particular). This gives me a great understanding of what these frameworks are doing.

автор: Benny P

21 февр. 2018 г.

This course brings you all the details to make your neural network training works better (faster, more accurate, etc.). The materials are presented nicely (as usual), not only the formulas but also the intuition behind them as well. Very good.

автор: Azamat K

13 авг. 2019 г.

Really cool course, review what i knew already. In addition learn more common optimization algos (GD with momentum, RMSprop, Adam). Get the sense of approaching the most tricky part hyperparameters tuning. Nice and smooth intro to Tensorflow.

автор: Ganesh c

16 мая 2019 г.

This course has been amazing. I have learning various interesting topics such as overfitting,techniques to avoid overfitting,Different optimizers and softmax regularization. Tensorflow framework is awesome. Looking forward to learn that too.

автор: Luca

20 апр. 2018 г.

Great course, Andrew Ng is just the best teacher for NN and machine learning. Assignments and quiz too easy, probably i will struggle on the implementing it on my own. But is common to MOOC so definitely the best deep learning MOOC out there

автор: vikram i

23 авг. 2017 г.

I wish these assignments would never get completed. Very nicely hand holding done through out the assignments so that you don't loose interest and also teach the intended stuff without watering down.

Thanks a lot! Recommend this course highly!

автор: Ramenga H

12 сент. 2017 г.

I'm a beginner in ML so i don't know much to say but this course teaches what it says it will do. In the middle of the course you may be having lot of questions about how it all fits together, but they almost always get clarified in the end.

автор: Ana J S

20 дек. 2020 г.


A lot of content to improve the knowledge about NN and related frameworks! As always a lot of guided interesting practices very applicable to real problems. I will continue with the rest of modules without doubt.

автор: Neeraj

28 февр. 2018 г.

A bit hard to follow but worth the time spent as this course helps to build the intuitions behind some of the most famous optimization algorithms and tuning methods by making the students work through them using only basic python and numpy.

автор: Kunjin C

3 сент. 2017 г.

Very practical course for implementing deep neural networks from scratch. The ideas of hyper-parameter tuning, regularization and optimization are very refreshing even for experienced deep learning engineers. Learned a lot from this course.

автор: Vihanga J

16 мая 2021 г.

The course was really interesting and helpful! This series of coursers guides even a novice to the field through a simple, easily understandable learning process. Many thanks to Coursera, DeepLearning.Ai, and the lecturers for this series.

автор: Pratyush S

28 авг. 2017 г.

Continuing the trend of magnificent course content, Dr. Andrew Ng walks us through some exceptional practical advice in implementing DL algorithms, detailing the concepts and the best-practices, and mentioning the pitfalls. Simple awesome!

автор: Dragos R

17 июля 2019 г.

It's a very good course for those willing to dig into the nitpicking of it. If you're really serious about this field, or if you're going to use neural networks often in your job, these lectures (and notebooks) can save you a lot of time.

автор: Neil O

12 янв. 2018 г.

This is an excellent class for understanding how to tune neural networks. I guess this will continue to be a valuable skill set until we have neural networks that can figure out optimal parameters to design and tune other neural networks.

автор: serge

10 сент. 2017 г.

I've worked with deep neural networks before for a while, but this course gave me a lot of new ideas, especially different tips and tricks on fine-tuning hyperparameters and speeding up the training of a deep neural net. Highly recommend!

автор: Ernest W

30 июня 2021 г.

The course is okay. The programming assignments really shows how tuning the equations improve neural network. At the end there is a quick introduction to Tensorflow. It's too shallow but I guess rest of specialization will teach me more.

автор: Aminur R A

3 мар. 2021 г.

Pretty much helpful. I was a novice and this course helped me a lot to start my jourmey in a right way. I will recommend others to come and join this course and hopefully you all can get more insights in your career in machine learning.

автор: Wong X Y

28 февр. 2021 г.

This course provides a lot of insights towards improving performance and accuracy of deep NN with clear teaching, step by step and practical assignments. Love the introduction of TensorFlow in this course. Hats off and thank you so much!

автор: Carlos A L P

4 янв. 2021 г.

Great continuation of the 1st course of Neural Networks where you explore more about NN and how to optimize the implementations / algorithms. This course explains again at a lower level grade how the hyperparameters affect the algorithms

автор: Federico E T

30 дек. 2020 г.

The introduction to Tensorflow and real tools to work with these complex aproaches is something I did not expect and is really good. It more accessible than the previous one ! And that makes it more applicable. I am happy with the course

автор: 潘Kiet

28 июня 2020 г.

Tuning is very important piece in DL. Thanks for this awesome course prof Andrew! A little peak into Tensorflow is quite eye-opening. Though it's only Tensorflow 1.0 but you will come to know later how powerful Tensorflow 2.0 has become.

автор: Michail T

27 авг. 2018 г.

This part is one of the most important to working with NN or DL nets. The instructor has achieved to teach a not so easy topic in an awesome manner so everyone is able to tune his networks as a professional. Can't wait for the next part.

автор: Adrian L

25 нояб. 2017 г.

I this course I learned how to improve Deep Neural Networks by applying different methods that help to speed up the convergence and to reduce overfitting. Also, now I have some basic knowledge about using TensorFlow. Thank you very much!

автор: Hussein J

8 дек. 2021 г.

Exceptional Course. I really enjoyed the explanation of Hyperparameters. Every tip, every piece of advice helped me to build better models. Moreover, I liked the introduction part of the TensorFlow framework. Thank you, Prof. Andrew Ng.