Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization(으)로 돌아가기

4.9

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50,728개의 평가

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5,725개의 리뷰

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....

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.

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.

필터링 기준:

교육 기관: tgerlach

•May 30, 2018

This lectures + programming examples are very good for a kick start and to understand key concepts. I'm a mathematician, diving into deep learning. I really appreciate this course. The programming examples are valuable even if my python knowledge is on a beginners level. Thanks!

교육 기관: Dipanjan G

•Feb 03, 2020

This again is an excellent insight on the hyper parameters and deep learning frameworks. The extreme prowess in the subject but at the same time a very lucid and relaxed style of teaching from Andrew helps quickly grasp these difficult concepts. Looking forward to much more!!!

교육 기관: RAGHAV S

•May 25, 2018

This is such a crucial course to build upon the fundamentals of Neural Networks.

Especially the intuitions that Andrew has provided really add to the arsenal, I'm so glad I took this course.

Looking forward to the other courses in this specialisation.

Thank you Andrew/Coursera :)

교육 기관: Erik E

•Oct 02, 2017

This is a great course!! In this course a lot of the previous concepts start to be refined and streamlined for efficient implementation. I feel like this course gave me a better handle on the concepts that have been building since my first Machine Learning course by Andrew Ng.

교육 기관: Dishant G

•Sep 28, 2019

Very well explained each and every concept only I had struggle in gradient checking and every other video and quizzes are great. I hope after doing these courses I will definitely get a good career start after my graduation.

Andrew Ng Sir is Greatest teacher I have found yet.

교육 기관: Miguel P d L

•Nov 26, 2017

Excellent course, even using intuitions Prof. Andrew Ng is able to communicate the very details of the different regularization approaches, as well how to do a good hyper-parameter search. Finally it introduces the TensorFlow framework with a very nice programming assignment.

교육 기관: Japesh M

•Jun 10, 2020

The Deep Learning course is in great flow and can't get any better than this. I highly recommend deeplearning.ai specialization on Coursera for all the aspiring Deep Learning practitioners. Trust me you'll learn everything, right from the fundamentals to the advanced topics.

교육 기관: Yang Z

•Dec 15, 2019

This course gives learner a high level strategy in tuning hyperprameter. It teaches me not only the knowledge but also the intuition about the processes. It is also great to learn how to use Tensorflow framework in training models. Great job deeplearning.ai team and Andrew!!

교육 기관: Kai-Peter M

•Oct 28, 2019

Great course!!! The best online course I have ever taken! I enjoyed almost every day I participated in that course, really an educational treasure! It is so comprehensive and detailed at the same time. Due to the good presentation of the topics it was really understandable.

교육 기관: Saif M P

•May 09, 2020

I have really learned a lot of things! It surely took 3 weeks to complete all the things, it was tough at some points, but if I didn't do this course, I might have some regrets that I didn't achieve all the knowledge. Thanks to Mr. Andrew, he is really a very good teacher.

교육 기관: Mandar K

•Jul 22, 2019

Wonderful course and material. Andrew has a great way of explaining the topics in the simplest way. Although I had some issue with understanding the optimizers, I learned a great deal. However, This course needs a revamp using Tensorflow 2.0 for the tutorials :) Thank you

교육 기관: Amit G

•Nov 06, 2017

there is a lot of materiel that is being discussed during the lectures, and all of it seems like it could be really relevant. I am missing a consolidated course deck - ie something like a deck of slides on all the important concepts that are being discussed, for reference.

교육 기관: Kiet L

•Aug 27, 2017

Another awesome course by Andrew. I wish he was my professor in my grad school. I hope Coursera publishes all the notebooks + data on public github so I can redo all the exercise again. Too much info to digest in short amount of time. I can't wait for RNN and CNN courses.

교육 기관: Syed M H J

•Jan 09, 2019

Easily the best course on diving under the hood of how a Neural Network actually works and how to tune to the satisfaction of our results.

A no brainer for sure. The best part the exercises. You MUST do the exercises to understand thoroughly how the systems actually work.

교육 기관: WAN L

•Aug 01, 2018

I like this course, it details basic while popular technique we need to optimize neural networks. also the lectures on different optimization algorithms are very helpful for you to know details on how they run when we choose these algorithm in frameworks like tensorflow.

교육 기관: Arun G

•Mar 22, 2020

Excellent course, giving a very good insight into how to approach building a deep neural network, the concepts of various parameters, tips on how to best achieve a good algorithm and a step by step walk through of the different algorithms, parameters and optimization.

교육 기관: Yash M B

•Oct 22, 2019

Quite detailed curriculum. It is a great continuation for course 1 of this specialization series. As usual, Prof. Andrew Ng is there to guide our way throughout the course duration. A really fun and intriguing course which can lead to course 3 as a proper continuation.

교육 기관: PeterStephenson

•Jun 26, 2019

This course was perfect for me. I thought it was a good balance between theory and practice. I don't think I'm ready to start building NN's from scratch, but at least now I know how to get started. Also, I now have an understanding of the complexity of a ML project.

교육 기관: SHUBHAM G

•Jun 18, 2018

Mini Batch/Adam Optimization concepts was very well explained. I was expecting the detailed derivation of the backpropagation for the batch normalization case. Overall it was a great course and it greatly improved my understanding about concepts used in deep learning.

교육 기관: Favio A C

•Nov 03, 2017

4.5/5 A diferencia del primer curso que es una continuacion del de Machine Learning de Andrew Ng , aqui vemos una evolución del contenido , se pasa a ver miniBatch Gradient Descent, Regularizacion , Momentum , Adam , y un inicio a tensorflow

realmente un MUY BUEN Curso

교육 기관: Huaishan Z

•Oct 01, 2017

Through the class, the tuning of Hyperparameter is detailed introduced and more important is that why it's tuned is very clear. Suggest persons study deep learning to study this class carefully.

Expect to have more info from the current study in University or College.

교육 기관: John R

•Jul 24, 2019

I guess the difficulty is what you make of it, with further studying and dedication, but I would like to encounter more challenging assignments, where one has to code entire cells for instance, as opposed to a single line here and there.

But everything else is great!

교육 기관: Janzaib M

•Mar 04, 2018

Contains very good understanding of Hyperparameters and their tuning process.

Secondly, teaches very well the mathematics of optimizers such as GD, SGD, GD with Momentum, GD with RMSProp and ADAM.

Finally, a small glimpse of Batch Normalization.

Highly Recommended!!!!!!

교육 기관: Frank I

•Aug 25, 2017

I had previously used optimizers with momentum and variance momentum (Adam) with the understanding that they helped without knowing exactly how. This course cleared up all those tiny details and has left with with a greater appreciation of neural networks in general.

교육 기관: Thomas N

•Oct 09, 2019

This course broadened my understanding of what really happens when driving the cost function closer to its minimum and techniques to go there faster. I found this course instructive and the programming excercises helped a lot to digest the learnings from the videos.