Learner Reviews & Feedback for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization by DeepLearning.AI

This course is continuation and a requirement of the first course. Really like the learning style of how first course and the first 2 weeks of the second course taught neural networks by doing all the math and calculations manually and finally introduced Tensorflow with parallels of what was taught in the class.

This is another excellent course in this specialization. I enjoyed the programming assignments. The instructions, tips made Tensor flow coding section to be easy . However, few blocks consumed more than few hours, due to placeholders. logic and the TF documentation is overwhelming. I am proceeding to next course.

This course is harder than the previous one. It teaches more details of tuning parameters and optimization in deep learning. In the end it also teaches tensorflow which is really helpful. It's like a programming course, nerally all the commands have been already provided, so it's not hard to get the code correct.

As usual, Andrew is a great instructor. He taught very complex concepts in very simple language and used notations that were easy to understand and were consistent throughout the length of the course. WOULD DEFINITELY RECOMMEND. I am hoping to complete the specialization in less than a month. 2 down, 3 to go!!!

It is really good and teach me the basic understanding of DeepLearning back propagation and gradients optimization like Momentum, RMPS, Adam finally I learn how to use Tensorflow to train my model.

But there are some mistakes in the assignments and also in the grade so that it costs me a lot of time but useless.

It was a good course on understanding various hyper-parameters, some regularization method, optimization of algorithms, various gradients and gradient checking, batch - mini batch, exponentially weighted average , some tuning algorithms and finally a small introduction to deep learning frameworks. RECOMMENDED!

The course content was excellent. The only issue is that there were some glitches with the grading of the second week programming assignment, in that I could obtain the expected output, but with repeated submissions, there would be (different) sections which could not be graded due to unnamed technical issues.

5/5.Thank you sir for helping me in my career.I recommend everyone to go through this course if you really want to learn detail about hyper parameter 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.

Excellent content and very well explained. Thanks for this amazing course.

The course cover the building blocks of a Neural network. Andrew (and his team) did a great job by organizing the content in an evolving way in which you have the chance to build the knowledge from each piece of a (deep) Neural Network.

Practical programming lessons, and well-paced enjoyable lectures.

Comments:

Move tutorials on TensorFlow to Course 3, which was the most obscure part of the course. TensorFlow isn't as intuitive as other numerical toolboxes, so spending more time on the foundations of TensorFlow might reduce the learning curve.

Intuitive, in-depth (while not losing the big picture), engaging and well structured, with amazing assignments to revise and solidify everything you learn in the videos. These courses are awesome! Just one suggestion: It might have been nice to have more intuitions on BatchNorm in the assignments. Thank you!

A wonderful AI course! In most Ai courses we could only learn some specific algorithms. In this course, however, we could learn beyond AI itself. There are many practical AI skills in this course like hyperparameter tuning, regularization method and optimization strategies! I benefit a lot from this course!

It covers very good tips and tricks to build and enhance deep learning model.

Andrew is the best teacher for ML and Deep Learning, he covers all theory and practice simultaneously.

In this course you can understand all mathematical intuitions and implementation of neural network from scratch by your own codes.

Another great class taught by the incredible Prof. Ng! I have to admit there were time I felt a bit overwhelmed but by the time the course came to an end, I felt like everything "clicked". Very much looking forward to the next course and can't wait to dive deeper in ML frameworks. TensorFlow, here I come :D

It is a very good follow up course in this Specialization. It is about how we can improve our a accuracy/predictions by tuning hyperparameters, using better optimization techniques and it also talks about deep learning frameworks. Overall it was a good course. Thank You Andrew Ng for this wonderful course.

Good second course to understand how we can improve our deep learning models with a good hyperparameter selection, some regularization techniques to reduce overfitting such as dropout, l2, early stopping and some optimization techniques for when we have a large datasets like momentum, RMS prop, adam, etc..

This second course in the specialization is really great. I have gained a lot of insight in hyperparameter tuning and the reason why they work (or don't ;-). It is much easier now to understand what models are doing and why we need certain techniques. This is again one of the best courses for deep learning.

Fantastic course providing a broad overview of hyperparameter tuning in deep neural networks. The introduction on TensorFlow is informative. Looking forward to the three remaining courses of this great specialization on machine leaning. Thanks Andrew and their assistants for putting those courses together!

I really liked the course. The forum is very helpful navigating programming errors during the assignments.

A thing to improve would be to get the feedback from the forums to the lectures. Specially in corrections that should be made to the programming assignments that don't match the expected result. Thanks

Provided a lot of deeper insights passed over in the previous course in the specialization. Between this course and the previous course, you feel like you have a very solid beginner's understanding of deep learning, but one that is also practical enough and comprehensive enough to start coding on your own.

Andrew Ng is a great teacher and will get you excited about improving deep networks. In this course you will get to know how to increase performance of your network. Essential course for deep networks specialists and amateurs. Additionally you will get to know most influential people befind the technology.

All possible area of Improving Deep Learning models got covered in detail. I liked the lucid and intelligible way of explanation . Since the topics were vast to cover , I would recommend to get the course extended by 1 week with one more programming assignment on using tensor-flow with a capstone project.

This course will becoma a foundamental course for people that aim to work in the machine learning / deep learning area because it presents clearly the recent innovations in the deep learning. For production environment people will probably use open source framework, but this course clarify what is behind.

Excellent course if you are passioned about Deep Learning. Walk you through the most basics on how to tune the model parameters so that you can reach the highest accuracy for the model. The lecture is simple and well ordered. The TensorFlow introduction part is more exciting. Overall a wonderful course.

Excellent course! Not only does it address critical deep-NNs training issues providing a clear exaplanation around why these tunings are needed, but also provides some empirical advices (e.g. on level of importance on the hyper-parameters, typical values etc) that can be valuable when training depp NNs.