It explains a bunch of complicated maths and methods in a way that is at least comprehensible by mere mortals, though not necessarily easy. Put another way, if this course doesn't enable you to understand how to tune and optimise deep neural networks, then you probably never will.

The content taught in this course is really valuable because it explains a lot of what is going on behind the scenes in the existing Deep Learning Frameworks like Tensorflow, Keras, etc, and enables you to be a lot more competent and confident in producing effective models in a time-efficient way, than if you didn't have this knowledge.

It also seems to have been built by peopel who not only know the material intimately, but who recognise that many of the learners are very time-poor.

I took the Machine Learning Course from Stanford with Andrew Ng a few years ago and enjoyed it but I was also somewhat overwhelmed by the math. In contrast, this is my second course in the deep learning specialization and I feel like so far the courses have struck a good balance, introducing core concepts and derivations for things but also making sure I get guided practice along the way, and also not moving straight to frameworks but having students code more or less from scratch first. I'll probably need some practice on kaggle or other datasets as well as reference to a few other learning materials to feel like a strong practitioner, but this gives the tools to make that possible and I'm very satisfied with this result.

All the courses in the Deep Learning Specialization are very good and met my expectations. I was guided through the nitty-gritties of neural networks, fortunately with a strong emphasis on Computer Vision (my area), deep diving in coherent coding exercises. Prof Andrew, as always, managed to connect the points between theory and practice, recollecting the concepts treated in past lectures, while showing how Tensorflow operates and how to use it. If you ask me, I'd say that the slides of the Machine Learning course used to be better than the slides for the 4 courses in this specialization, in the sense of being useful as studying guide for the future. The current slides only make sense to those who went through the course.

Knowing this makes the difference. How do you evolve from being a monkey behind a keyboard knowing how to tensorflow a NN to homo sapiens? The concepts provided in this course will make the job.

pros: + workflow to address and optimize your supervised learning problems

+ wide and easy-to-get overview on most essential concepts

+ improves your understanding of NN; those who are already familiar with these concepts might still benefit from this clear and insightfull presentation

cons: - programming assignment will not suffices to give you a sufficient knowledge of tensorflow to make your own applications, you should integrate a bit. (However, mastering tensorflow is not the intention of the assignment).

Très bon complément de la partie 1 avec systématiquement une présentation intuitive puis technique (pour programmer à partir de 0) des concepts ..Le contenu est très dense, je conseille donc de prendre des notes et d'essayer de refaire quelque démonstration! Je conseille de bien lire tous les codes en détail ligne à ligne.

Par contre l'introduction à tensor flow n'est pas très bien expliquée selon moi (seul petit point faible) mais ce n'est pas l'objet de cette partie je pense.

Attention la deuxième partie ne comporte pas de sous titre français. Mais finalement en ayant un niveau moyen en anglais, j'ai facilement suivi avec les sous titre anglais car il y a souvent des schéma et le même vocabulaire revient souvent.

Good pace for beginner as the last one. With step by step teaching us a lot of useful skills to train our model much faster, Andrew starts to put more attention on practical field, and rather than giving us many equations, he as before likes to use some vivid examples for giving us an intuition, which I think is very helpful to understand those scientific words of computer science. But it doesn't mean, that this course is perfect, even I gave a full point to it. The subtitles have a lot of mistakes and the audio is also poorly processed. Sometimes, you will hear the same words twice.

But in a word, this is the best course for the beginners and the engineers who are willing to know something about ML and AI.

I loved it. It showed me practical aspects of machine learning, including how to chose the hyperparameters and how to use tensor flow. My only complain is that I'm not sure how much of this information I will retain given that the practical exercises are guided. They build a lot of the functions for you. Still I'm giving it 5 stars because I have not seen this problem solved so far in any other Coursera course. They need to find a balance between teaching you a lot and making it easy enough for most people to be able to pass the exam and not get stuck in the details. Probably they could offer extra practice automatically graded exercises for those of us who want to make sure we won't forget the material.

I'd highly recommend this course to any of my colleagues interested in Deep Learning.

It is a great followup to Deep Learning and Neural Networks.

My one 'complaint' is that the mathematical depth is too shallow for someone like me (PhD, Mathematical, Computational and Experimental Physics)

It would be great if there was a course that was targeted to people with advanced STEM degrees, and years and years ( 4 decades in my case) of software engineering experience, where more time was spent on the mathematical framework, and the basic algorithms; that way, I'd have the satisfaction and pleasure of constructing the full algorithm implementations myself.

That being said, once again, Andrew is a great teacher.

I was very impressed with the quality of Dr.Ng's teaching; simple enough to build confidence in your understanding of the inner workings of neural networks yet thorough enough to prepare you for deeper study (academic or otherwise). For $50 this course is a steal; you could go gather all the information & struggle through it yourself but be prepared to spend a lot of time & effort sifting through mis-information.

After taking the 1st coarse I was impressed; course 2 follows in a similar vain. Some of the courses offered through Coursera are more polished than others; if you're at all curious in deep learning, or even if you've already begun your studies, do NOT miss out on this opportunity.

Previously, I have taken online classes before in Machine Learning by going the cheap route (Udemy, blogs, youtube) and you get what you pay for. Andrew Ng explains it the most thorough, easiest, and simplest way possible. Presentation material is very understandable. Great class for new machine learning learners. Highly recommend it. The only downside is that the programming exercises are little too easy in my opinion. I feel like the best way to get your hands dirty is to do actual projects (do your own projects). These lectures are good for intuition and background of different types of Neural Network architectures. Other than that, Great material. Thanks Andrew!

El curso está muy bien impartido por Andrew NG y te permite adquirir muy rápido conocimientos sobre los puntos clave para mejorar el aprendizaje con redes neuronales de una forma genérica. La práctica de programación con la plataforma tensorflow de python es muy valiosa, aunque se hecha de menos una mayor profundidad en el uso de las herramientas disponibles de tensorflow y otras utilidades de python para redes neuronales. El curso utiliza como ejemplos didácticos y prácticas la aplicación de redes neuronales al reconocimiento de imágnes, pero estaría bien ampliar los ejemplos con aplicaciones prácticas a otros campos como puede ser un modelado de un proceso físico.

This course has been the game-changer in my understanding of the concepts of hyperparameter tuning and optimization. The conceptual knowledge of various tuning techniques along with the theoretical and practical information about the algorithms like GD with Momentum, RMSprop, and Adams have made me confident.

The introduction to the machine learning framework along with the assignment focusing on Tensorflow has also made me confident to learn more and prepare better projects.

As I have mentioned in my previous course review as well the programming assignments are extremely useful and very much important for the understanding of various deep learning concepts.

Andrew ng is simply the best. He is by far the number one teacher for ML. He explains the materials in such an intuitive way like no one else. I think that for my needs, he introduces just the right amounts of math and practicality.

This course really showed me how gradient descent optimization methods work. From before, I knew about these optimization methods and what they do, but never fully understood them. After taking this course I feel much more confident using them. The transition to tensorflow is done at the perfect time. After writing NNs yourself using numpy (first course in this specialization), you can truly appreciate tensorflow.

Thank you Prof. Ng and Cousera for this amazing course! It was totally helping me with my career development and with feeding my interest in Machine Learning science itself!

Upon cancelling my subscription of this course, I would to like to let you know that, it had to happen simply because coursera's webpage wasn't smoothly running with my current network based in China. The browsing and watching experience wasn't very idealized and it sort of discouraged me from keeping using it. If the issue could be solved, I am sure more people in my location will be attracted and encouraged to use Coursera.

Stay safe and I hope you all the best,

Great in depth explanation from ground up on how to tune parameters. Including many personal experience by Andrew Ng throughout years of experience of handling AI projects. Before going into quick shortcut by using the Tensorflow libraries, it is really useful to know the concepts and intuition on how Deep Learning works from ground up. Also teaches you how to solve with many problems in overfitting, underfit, reading the results. In short, his experience that has seen many researches spent too much time into creating projects, and end up hitting brick walls, is summarised with suggestion on how you can avoid that in your AI project.

The course is amazing, the instructor explains everything with a good level of comprehension. All the covered topics are easy to understand, and the tips given are valuable. The examples given are new including also the information seen in the previous course, so you have a review of parts of the content you have seen. Although he keeps using the cat example, he introduces new other applications that are useful.

I wish that the last assignment were a little bit harder, or that we could use our previous knowledge and complement it with this new, but I suppose that it is this way because of pedagogy and it focus on the topic.

This course is wonderful! Hats off to Prof. Andrew. The explanation for each topic is step wise and well organized. Every detail and reasoning is covered up. Even though there is a lot of content in this course, it is easy to remember and understand most of it, because of the way it is explained. The programming exercises are well planned and guide you through the code well. This course also has a brief introduction to TensorFlow, which is explained well through its programming exercise. Overall, this course is really good for those who are looking to master the methods to improve and optimize Neural Networks.

The second module of the deep learning specialization is excellent. You will learn best practices regarding hyper-parameter tuning, how regularization can be used in Neural Networks, optimization algorithms such as Momentum, RMSProp and Adam. In addition, you will be able to build your first machine model using tensor flow as part of the practical assignments. Professor Andre NG explains the content clearly and it is very pleasant to watch his lectures. I definitely recommend this course because it will give you confidence to build your own models and will provide several additional tools in your tool-belt.

Anyone who wants to excel in the field of AI must absolutely follow the 5 courses of this specialization in deep learning offered by the deep learning.ai indeed the courses of the specializations will bring you a deep knowledge of the field because it is important that all those who want to embark on a career in AI, understand the fundamental concepts very clearly, as it will help them to design powerful AI models ready to be exploited for me. I'll help you say start now, not tomorrow. Tomorrow is a loser's excuse. many thanks to deeplearning.ai and coursera to share this knowledge...Orson Typhanel MENGARA

As with the first course in this specialization, the presentation was spot-on and the content was rich. The practical application is a wonderful tool for learning and I feel as though I have learned much more than I might have in a traditional classroom. I also feel that this course was slightly more challenging than the first, and introduced me to a few concepts I hadn't heard of before despite other research and development in AI/ML. Thank you so much for your dedication to sharing your knowledge and introducing new students to some of the brightest minds in the field with the optional interview videos.

It is very important for students to feel that the instructor and the education system are in their side, not in the confronting side. I feel like the whole deeplearning.ai course, from design elements to teaching, are in my side, they are here to help me learn. It is great!

The negative side is using TensorFlow1. The python package is clearly an interface to a lower level language, and thus either some background in that language is needed to understand the process better, or it is better to migrate to TensorFlow2. Why TF1 when version 2 is available and it is much easier?

small description error on the last project. tensorflow tutorial project.

X, Y = create_placeholders(12288, 6)

print ("X = " + str(X))

print ("Y = " + str(Y))

X = Tensor("Placeholder:0", shape=(12288, ?), dtype=float32)

Y = Tensor("Placeholder_1:0", shape=(6, ?), dtype=float32)

Expected Output:

X Tensor("Placeholder_1:0", shape=(12288, ?), dtype=float32) (not necessarily Placeholder_1)

Correct this from Y Tensor("Placeholder_2:0", shape=(10, ?), dtype=float32) (not necessarily Placeholder_2)

to:

Tensor("Placeholder_2:0", shape=(6 ?), dtype=float32) (not necessarily Placeholder_2)

This course goes into the various parameters and hyperparameters of deep neural networks, as well as suggestive values for ones we can use. This course is short in duration, but a lot of content is developed in here. It touches in on Tensorflow. The template based assignments provide great intuition for getting right on to the topics being taught, however, I feel there should be scope for more programming assignments where the student should be able to write more of that template as well. All in all, Andrew Ng is a great teacher and it was a pleasure to learn from him.

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.

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.