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.

Perfect teaching material and syllabus. The lectures are very easy to understand and the way Andrew takes the student through each topic creates a level of understanding I did not have before. Thank you Andrew Ng, Coursera, and Deeplearning.ai

One thing I would like to say is that there are quite a few problems with the sound, especially the trailing emphasis on the 'sss' sounds is very annoying and distracting. Also, there should have been a pointer visible on the screen. As the videos now are, when Andrew says something like "... for this equation here..." I have no idea which equation he is talking about. Kindly resolve this problem. Otherwise this course is 10/10.

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.

Another excellent course by professor Andrew Ng, short, on point and clear videos that go into the subject of optimizing Deep Neural Networks.

Like the previous course of the specialization the programming assignments are coded and submitted in the browser using Jupyter notebooks, the coding language used is python and for the math the python library "numpy". In the final week of the 3, an introduction to Tensorflow is made.

It is worth mentioning that some interviews with influential people on the field of DL are included and make the student fall in love with DL even more. Excited to see what's next in this specialization.

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.

I'm so appreciative for the course content and the direction that it has moved me in a deeper understanding of NNs and tuning and optimization. I don't know about other students but I am a serious student that labors to comprehend all of Dr. Ng's videos. The labs are very helpful and gently focus the student on code lines that relate directly to video content. The very last lab compelled me study the TensorFlow function library and find what I needed to get the one line of code correct for computing the cost. This took time but was valuable. I'm now looking forward to more experience with the TensorFlow framework.

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.

Having read some of the other reviews I understand why people feel like the course was too easy and that at the end of it they don't feel confident enough to implement some NN models from scratch. Just to be clear, I feel the same way. However, the intuitions I gained from this course, as well as the last one, helped me to understand at every moment what was being done in TensorFlow, and why, during the last programming assignment. For me this assignment was a smooth introduction to TensorFlow. In general, the videos are clear, concise and prepare you well for the quizzes and the assignments.

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.

first of all, I was very pleased with the organization and the programming of the course. It was compact with fundamental theories and concepts for improving deep neural networks. In particular, this is my second course I have attended on coursera, and I strongly believe that it helped me with my bachelor's thesis in mechanical engineering at Aristotle University of Thessaloniki. Above all, it was a fast-paced lesson including a fast "learning rate", which was quite suitable for me as an engineering student who is trying to finish his first diploma (bachelor) degree.

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 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.

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 ;)

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.

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!