Awesome course! Just realized here that the programming of neural networks are already much easier than in the early 2000s due to the availability of OSS libraries

Awesome course! Dr. Ng really familiarized me with the basic algorithms behind Deep Learning. Can't wait to keep on going through the Deep Learning specialization.

Andrew Ng is so great a mentor that he helps me understand many seemingly complicated concepts and mathematical manipulations in deep learning through an easy way.

Amazing lectures! Very well understandable! I was able to understand everything, even if that was not the case before the course! I could only recommend this spec!

This is a awesome course that greatly simplifies and explains the concept of Deep Learning and Neural Networks. I recommend it to anyone interested in the subject.

Great course, learned more than i expected.

Wish to learn more about applications in the industry, maybe introduce a little bit of real cases and provide some links

This course helped me visualize the inner working and math behind Neural Network. I would definitely recommend this course to get the intuition of Neural Networks.

The course provided a very good basic background of Linear regression, deep learning and neural networks. I am motivated to finish the remaining 4 courses as well.

Excellent course with correct programming exercises. Plan to do all Week 3 and Week 4 exercises inclusive of coding own utility functions. Enrolling second course.

I am very happy after watching all the lectures and also understand the basic concepts while programming. This is a worthy course for acquiring the deep knowledge.

Really good introduction ! After having followed the Machine Learning introduction however few things looked like déjà-vu but it was not useless to learn them back

Very good and very useful! This course helped me to improve my understanding of neural networks and improve knowledge and organize materials in my mind. Thank you!

Great course as usual! Clear and friendly to all public. It helps you to build your intuition, which I think is very important in all subjects. Highly recommanded!

Good introduction to concepts of deep learning. Quizzes are helpful, and coding assignments give thorough instructions and set you up for well-decomposed programs.

One of the best courses I have ever done. The videos are very nicely made and the programming assignment cover all the concepts covered in the lessons beautifully.

Un cours très didactique avec une progression pas à pas. Les exercices sont guidés. Il est bon de les regarder ensuite dans leur ensemble pour bien les comprendre.

Excelente, muestra de manera practica, didáctica y metódica como se define y se realiza una red neural para reconocimiento de imágenes, gracias por el aprendizaje.

It is the best class to take you have basic machine learning knowledge and python programming experience.

It is especially good for those who have a full-time job.

Very detail derivations and directly explain many concerns I have about deep learning. A excellent part is the programming tips to develop deep learning algorithms

very detailed materials and lectures are easy to understood. I would recommend study with the cs230 from Stanford which is the more advanced version of this course

very good course. the programming assignment is actually teaching me how to do the programming, not only focusing on grading my result. excellent lecturer, thanks!

I really enjoyed how simple the explanations were and the programming assignments helped me understand the material better. Really helpful for any interviews also.

Terrific course! Andrew Ng is a great lecturer, making concepts clear and concise. The notation in this course is also vastly improved over the original ML course.

It is an excellent course for start with neural networks and deep learning. And it also helps better understand machine learning concepts like logistic regression.

A nice hands-on introduction to neural networks and first steps into deep learning. I liked that very much and look forward to what comes in the subsequent course.