Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization に戻る

4.9

星

55,251件の評価

•

6,320件のレビュー

This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow.
After 3 weeks, you will:
- Understand industry best-practices for building deep learning applications.
- Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking,
- Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
- Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance
- Be able to implement a neural network in TensorFlow.
This is the second course of the Deep Learning Specialization....

NA

Jan 14, 2020

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.

CV

Dec 24, 2017

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.

フィルター：

by Jong H S

•Oct 02, 2017

At the time of writing this review, I have completed 3 of the 5 courses. I personally think these 3 courses are not merely courses to fill up the specialization. It is a journey, an incredible one. I will write metaphorically. My journey so far is like becoming a magician with Course 1 on how to become one, then went on to Course 2 to learn from the master magicians, their secrets revealed and Course 3 on what to do to put up a good show at Las Vegas trying to fool Penn and Teller. This specialization is my treasure vault. Great job to Prof Andrew Ng and team.

by Vincent F

•Jan 23, 2018

This course provided me with an understanding of the large number of hyper parameters that have to be tuned during a deep learning project. It gave me an insight on when different techniques like regularization and (the many different forms of) optimization need to be applied. The only quibble I have is that the material on the choice of the number of layers and the number of hidden units per layer was thin. Given that these values have a great impact on the speed of progress in a deep learning project I would have liked to have seen a little more emphasis on them.

by Ernest S

•Nov 05, 2017

This course offers ground knowledge in all mayor concepts of non-recursive neural network and is excellent preparation to further exploring of this topic. Lectures cover broad choice of topics and discusses many problems you might encounter during your journey. Professor Andrew Ng explains theory in a way which builds good intuition and gives you building blocks for face the challenges of machine learning. If you are fluent with calculus or have academic background and expect to discover math behind the scenes I think you will be content too. I surely was.

by Aditya B

•Jan 12, 2019

The concepts has been explained in a fantastic way. But few suggestions:

-> After every lesson, I would love to have more pop quizes. This was the case with course 1, but I didnot get any pop quizes for this one.

-> In the quiz assignment, it would be nice to have an explanation or justification section, which will explain that why the option selected is a correct one and why the other options are incorrect. I know we can have the same discussion in the forums, but such an explanation ( one liner should be fine) can provide a good instant knowledge boost!

by Robin S

•Jun 09, 2018

Another very well done course. You do a good job describing the benefits of Batch Norm, a lot more intuitively than presented in Szegedy's paper, which is pretty math heavy. However, I did notice one little ERROR on the Tensorflow project page, albeit an insignificant one. Double check that the expected output shape for the cell that outputs the shape of the training set and testing set. One of the expected outputs said that the test set should have 10 possible classes, when the dataset is for 0-5 fingers. This would be a very strange looking hand ;)

by David M

•Sep 01, 2017

This is a practical course on how to work with neural networks. It covers a collection of "tips" and techniques, all grounded on a solid theoretical framework, to make a classifier train faster and be more accurate. The explanations are all engaging and interesting, and the assignments are rather easy.

The knowledge gained from this course is probably what everybody working in machine learning already knows, but if you are new to the field this is a great way to get up to speed fast and start implementing neural networks for your own projects.

by Jairo J P H

•Feb 01, 2020

El curso es muy bueno, particularmente estoy muy agradecido con COURSERA, por darme la oportunidad de hacer los cinco cursos de la Especialización en Deep Learning con ayuda economica y permitirme tener acceso a este tipo de capacitacion y certificacion. Muchas Gracias…!

The course is very good, particularly I am very grateful to COURSERA, for giving me the opportunity to do the five courses of the Deep Learning Specialization with financial aid and allowing me to have access to this type of training and certification. Thank you very much!

by GEORGE A

•Mar 05, 2019

Pretty solid class, learned a lot of basic concepts. The class won't go into a lot of mathematical details about the algorithms however, there is enough intuition provided in order to understand the inner workings of the algorithms and the logic behind them. The only con I have is that some of the programming exercises look outdated with the current versions of the notebook. For example, in my last exercise I couldn't make the NN with tensorflow to work properly but got 100/100 nevertheless.

by Matei I

•Feb 02, 2019

This course covers details about neural network implementations that are extremely useful in practice. In fact, after completing week 1 and learning about vanishing gradients, I was finally able to debug a NN implementation that I had been struggling with. I'm also grateful for the introduction to Tensorflow. As with the previous course in this specialization, expect to be spoon-fed during the programming assignments. The course would be better if it let you think more during assignments.

by Pablo G G

•Sep 10, 2020

Awesome introduction and guidance about where to tweak your model...altho in my expirience Adam is all you are going to need. Missed some teachings about fine tuning thought iterations with scheudeles! Tensorflow has this funciong than can adapt on the go your parameters so your optimization can push that loss lower and lower. Adam optimizer works like charm with an schedule for learning rate!!(https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/schedules/LearningRateSchedule)

by 江小鱼

•Feb 12, 2019

This time , I finished Regularzation, I think this is a interesting experience, for you can implement your alg step by step, I get some magic(not black magic) alg, like RMS, momentum and Adam. At last, the most fascinating is to construct Tensorflow, just like a pipeline, step by step , and every step was made by only one line, from forward (without backward) to the model, Tensorflow is really black magic.

(I have to say Tensorflow is a bit difficult, forgive my poor English, thanks )

by Nathan Y

•Oct 16, 2017

Neural networks are not new. What we learned in this course is some of the critical implementation details/tricks from the past decades of making them work in practice. Going beyond gradient descent, types of regularization, hyperparameter searching we get to a set of robust tools that quickly find good solutions in extremely high dimensional spaces. As Professor Ng says, our understanding of optimization rules of thumb in low dimensional spaces doesn't carry over to deep learning.

by José A

•Oct 31, 2017

Seamlessly continues the previous course. If you know the basic structures of Neural Networks, how to initialize weights. Sigmoid, Tangenth, activations, and so forth, this will help you understand terms such as L2 regularization, gradient descent with momentum, RMSProp, Adam, Exponentially weighted averages, and many others.

Don't let the 3 weeks set you off. It has a lot of micro-content material that works on top of the previous work. Thanks to all the mentors for this great course.

by Raimond L

•Aug 20, 2017

Really nice course. A lot of good information about how to prepare and divide data for training, hyperparameters optimization strategy, regularization techniques, learning algorithms, mini-batches, batch-normalization and more... Very useful information with clear explanation !!! Highly recommended course.

Very positive course, except tensorflow practical assignment, which caused some stress, because for me that framework is a bit alienating, forcing to look into manual every minute.

by P M K

•Nov 26, 2017

Hi, The course content was definitely good and it helped to understand a lot of internals quite easily. I, however have one suggestion, the Introduction to Tensor Flow looked quite fast and could have been done in a better way by giving more slides about TensorFlow and then going on to the examples. Please ensure that you correct any errors pointed by the members taking this course, so that it benefits others and avoids wasting of time and reduces frustration at times.

Regards, PMK

by Sachita N

•Jun 18, 2018

Professor Ng explains the most complicated concepts in the most intuitive fashion I have ever seen. The explanations are simple, straightforward and they encompass so many perspectives and alternatives to doing things. The exercises are immensely educational - they strike a great balance between guiding the student and letting them figure stuff out on their own. This is a great specialisation and I would whole-heartedly recommend it for anyone wanting to start with Deep Learning

by kindalin

•Jul 31, 2019

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.

by Joppe G

•Aug 13, 2017

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!

by RAJEEV B

•Nov 18, 2017

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 :).

by Marcel M

•Jun 01, 2018

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!

by Artem M

•Apr 22, 2018

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.

by Daniel C

•Jan 14, 2018

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.

by Pedro B M

•Feb 04, 2019

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!

by Johnathan T

•Sep 01, 2017

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!

by Anton V

•Jun 13, 2018

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)

- プロフェッショナル認定
- マスタートラックの修了証
- Google ITサポート
- IBMデータサイエンス
- Google クラウドデータエンジニアリング
- IBM Applied AI
- Google クラウドアーキテクチャ
- IBMサイバーセキュリティ・アナリスト
- Pythonを使用したGoogle ITオートメーション
- IBM z/OSメインフレーム・プラクティショナー
- UCI（カリフォルニア大学アーバイン校）応用プロジェクトマネジメント
- インストラクショナルデザインの修了証
- 建設工学およびマネジメントの修了証
- ビッグデータの修了証
- 分析のための機械学習の修了証
- イノベーションマネジメントおよび起業の修了証
- 持続可能性と発展性の修了証
- ソーシャルワークの修了証
- AIと機械学習の修了証
- 空間データ解析および可視化の修了証