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

4.9
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Оценки: 59,722
Рецензии: 6,910

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

NA
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.

HD
5 дек. 2019 г.

I enjoyed it, it is really helpful, id like to have the oportunity to implement all these deeply in a real example.\n\nthe only thing i didn't have completely clear is the barch norm, it is so confuse

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101–125 из 6,854 отзывов о курсе Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

автор: kindalin

31 июля 2019 г.

This is the best course I have ever seen. The previous mooc class gave me some bad impressions, which is be created by some scholars for KPI. I believe that such a well-designed course will eventually replace the traditional curriculum. This is also a good hope for our students in non-brand schools.

The only downside is that the coursework instructions are too detailed as many people reflect. I can see a lot of good and hard designs in it, but I hope it can have a better form.

автор: Joppe G

13 авг. 2017 г.

This course is simply brilliant. You start with implementing the low-level functions that make up a deep learning framework. It's only in the last assignment that you explore TensorFlow. At that point, you have a full understanding of what the API encapsulates.

This really gives you confidence in your capability to get started with your own projects, knowing that you can come back at any time to brush up on some of the lower-level details.

Thank you Andrew and the whole team!

автор: RAJEEV B

17 нояб. 2017 г.

The assignments are very good. All the parameter update methods are explained in a very good manner. I would recommend it very strongly for anyone who is looking for an in depth understanding of why we do what we do for tuning, regularization, optimization of NN. All the implementation in the assignments is also from scratch, so, that really helps a lot. I felt this is better than Stanford CS231n course material, after all this is a whole course on this specific purpose :).

автор: Marcel M

1 июня 2018 г.

This course a practical way of fine tuning your model in order to improve on its performance. Rather than Deep Learning being a "so-called" black box. It turns out that Machine Learning models are not black boxes but rather there are proven techniques of not only finding out what happens in them but also to fine tune them in a systematic manner in order to improve on their results. It is an excellent course for the practical Deep Learning Engineer. Good Job and Keep It Up!

автор: Artem M

22 апр. 2018 г.

Found a lot of interesting details about NN that I did not know. This is a much better course than the first one. IncludesTensorflow exercises, which is useful. Nevertheless, proofs are still omitted for some results like initializations. It is not hard to google, but I bet lecturers could explain them much faster than diving into science literature. Otherwise, intuitional explanations of Adam using exponential smoothing, or physics analogy of momentum are just brilliant.

автор: Daniel C

13 янв. 2018 г.

True to the claimed learning objectives, the course Improving Deep Neural Networks shows some of the magic behind deep learning algorithms. The programming assignments solidify abstract concepts discussed in lecture videos. In fact, some portions like seeing cost decreases in real-time for Adam Optimization are truly eye-opening experiences.

One possible improvement is better editing of instructions and code comments of TensorFlow Tutorial Programming Assignment in Week 3.

автор: Alexander H S

7 апр. 2021 г.

Great course for an introduction for the topics discussed. Not having a math background, this finally allowed me to connect the dots between the techniques discussed in articles and the math behind them, as well as helped to demystify the all of the greek symbols thrown around. It would be nice to see the course upgrade the final assignment to use Tensorflow 2.x instead of the now deprecate 1.x, since Tensorflow has rearchitected the public surface of their public API.

автор: Pedro B M

4 февр. 2019 г.

As always Andrew Ng is very didactic explaining different and complex hyperparameter tuning techniques and optimizations algorithms, giving intuitive explanations and examples. I've been learning a lot in these courses! And more than that, the content is presented in such a way that motivates the student to go beyond and explore/try different implementations and problems to apply. I highly recommend the course for anyone who wants to become a serious ML practitioner!

автор: Johnathan T

1 сент. 2017 г.

This class was awesome! Thank you to Andew Ng and his team for putting this Specialization together. It is amazing for someone with so much experience in this field to be willing to share their wisdom with everyone, practically for free. The course content is filled with information that would have taken me years of to acquire. I am fortunate to have the opportunity to build a strong foundation in this field at a time when A.I. is becoming society's new electricity!

автор: Anton V

13 июня 2018 г.

A very valuable course to improve your understanding and develop a better toolset in using NNs. The instructor gives great tips on how to approach problems and explains the latest techniques very well. Also features a nice introduction to TensorFlow. As an experienced programmer I found this course to be a breezy and fast hands-on tutorial to get you going quickly if you are doing these courses to apply for something you are interested in (e.g. personal project)

автор: AVADH P

7 янв. 2020 г.

Excellent course!! Really glad to have taken this course as a part of the Deep Learning specialization. This course gives a breakthrough in designing neural networks and deep networks using a thorough understanding of all the major aspects to be considered. The course also helps in learning current industry-wide used opensource frameworks such as TensorFlow. The assignments are well designed to make the step by step understanding and exercise of the learning.

автор: Yuri C

22 янв. 2021 г.

I must say, I found this course amazing. I have read and also had contact already with other presentations on the topic. But Andrew Ng did an amazing job preparing the material. It is both didactic and mathematically precise, when it is needed. As a mathematician, I was expecting a more "programmer-oriented" course and I was delighted to get both, the explanations precision of the mathematical formulation and the tips and tricks of DL practice. 10 out of 10.

автор: Matheus B

22 сент. 2017 г.

Um dos cursos que mais gostei até o momento. Desde que comecei a estudar deep learning vejo se falar de muitas técnicas que pareciam impossíveis de compreender e implementar, mas esse curso não só ensina como implementar algumas delas, como também ajuda a entender o motivo dessas técnicas serem tão boas para os modelos de redes neurais, dando uma boa intuição de como cada método funciona. Além disso, apresenta e ajuda a desmistificar o framework tensorflow.

автор: Joe M

14 июля 2019 г.

This course was a great continuation of the first. The lecture pace is great (and ability to speed up or slow down the video speed helps a lot), the reiteration of past lessons helps with some of the denser materials, and the overall presentation is excellent. Also very nice that the problem sets aren't out to trick you! The material is new enough to many of us to begin with! The emphasis on practical application of the material is key (for me, at least).

автор: Nidhi V S

27 апр. 2020 г.

This course is very well designed and the instructor does an amazing job at explaining the concepts making it easy to learn, even for a novice in the field. This course helped me to get a greater understanding of Neural Networks. I learned how to enhance the performance of Neural Network by selecting appropriate hyperparameters, using regularization, using normalization and various other techniques. It was interesting to learn about the Softmax function.

автор: Ricardo S

17 дек. 2017 г.

The course covers an extremely important topic (I know I've been lost in hyperparameter maze before) , and allowed me to get a good feeling of what, when and how to use hyperparameters. I guess that to actually master the topic students will have to practice with their own models and data sets, therefore I think that getting actual practice on this topic would be out of the scope of the course, and thus I think the programming assignments were adequate.

автор: Holger O

23 мая 2019 г.

Prof. Andrew did it again! I took the "classical" Machine Learning course and I'm pleased to see that this continuation was as good or even better. A total equilibrium between the mathematical depth you need to understand the basis of the algorithms and the practical skills you need to put them in practice in the real world, in the exact amount for them to fit in a 18-hour course. As a starting point, this course is perfect! Eager to keep on learning...

автор: David F

16 сент. 2017 г.

These courses are awesome. Andrew Ng is a very clear professor and the interviews with other ML practitioners are enlightening. My one criticism is that the assignments are put on a plate for you so they're pretty easy to complete but then difficult to replicate in real life (since so much of the scaffolding was taken care of for you while learning). But maybe that helps to preserve the flow of the class, rather than getting you bogged down in details.

автор: Sergio B S

1 авг. 2018 г.

I began using Deep Learning Frameworks before this course, but...

I realise now, after this second course and the first one, that learning the maths behind Neural Networks helps exponentially to understand and internalize what is the real use of some of the most important hyperparameters and the what's and why's of good strategies to regularize models. As A.Ng repeat sometimes, this specialization help me "To get the intuition" to improve the models.

автор: Amit K

4 дек. 2018 г.

This is good course for the student, who want to do real stuff with NN. Some of the tricks are well explained like L2,dropout, adam, momentum, minibatches etc. I think these are much needed tricks if i need to implement and tune my own NN on my own problems. I prefer to have a second level of such course which really talks about challenges in real life NN and how to solve those. Once again thanks alot for the entire Team for pulling this together.

автор: Eleanna S

4 мар. 2018 г.

Very useful course. Gives great insight on the hyper parameter tuning, regularisation and optimisation. One request I have is to provide a docker image which we can use to run the exercises locally. Sometimes I found it hard to build the environment where I can run the coursework. Some of the installations are clashing and it is not clear what versions of libraries are used in the coursework environment. It sometimes requires unnecessary effort.

автор: Hugo v d B

26 сент. 2017 г.

In the second course of the Deep Learning specialization Andrew gets deeper into the different subjects of Neural Networks. Again he does a great job in explaining both the math and the way you can improve the outcoming of deep neural networks. The quizzes and assignments where helpful and not difficult at all. He also shows some good frameworks to work with and gives a nice introduction to Tensorflow. I'm looking forward to start with course 3.

автор: Parab N S

25 авг. 2019 г.

Excellent course demonstrating the ways to improve the accuracy of the deep neural networks. It had been the case with me that I could create an initial model easily, but getting an expected level of accuracy was difficult. This course has made it much easier for me to improve th performance of my deep learning models within a short span of time. I would like to thank Professor Andrew N.G. and his team for developing such a wonderful course.

автор: Xizewen H

5 окт. 2017 г.

This course is where the specialization really distinguish itself from Udacity's deep learning nano degree program -- the model fine-tuning part is very important and there are lots of details can be talked about, but Udacity somehow avoided going into details for it. After taking the Udacity's course first, I feel this course really helped me refreshed some knowledge I learnt as well as teach me much more. Definitely recommend this course!

автор: Ivanovitch S

28 февр. 2020 г.

This course is a bit more hard than the first one. I recommend using paper & pencil in order to reproduce all the equations. I gave five stars because the all material is very well described, however, the last part of week 3 must be improved, mainly that related to the practice assignment. There is no link between the Batch Norm and hyperparameter tuning with to practice assignment. Additionally, TensorFlow 2.0 should be introduced too.