Very good and well-designed course. The maths behind hidden layers, forward and backward prop is explained amazingly so that people with the non-mathematical background can also develop the intuition. I would highly recommend this to anyone delving into the deep learning. Also, do the assignments. They are well designed and help one to learn the basic coding.

Andrew Ng is a superb teacher and this course is extremely well put on. Everything is meticulous and precisely curated for a very good flow of concepts and ideas. Although the subject is not really on the easy side, he somehow manages to present everything well and smoothly. Thank you, Andrew, for taking the time and effort to create this really great course.

This is an excellent course. I really appreciate the course organizers made the materials open to auditors. Even though I have quite a bit of machine learning background, I felt this course really enriched my prior learning and new insights into the field. The homework problems are the most helpful part. Andrew's lectures and pedagogy are also very effective.

This course is very good. Andrew Ng was able to explain the formation of Neural Networks from scratch all the way to using it to classify cat images. He also tries to explain all the mathematical concepts behind it.

A small warning. Personally, I feel like it would be hard to complete this course without some background in Python, Linear Algebra and Calculus.

Walk me through the essence and implement of Deep Learning in plain language.

Definitely good for learner without much programming background or deep math knowledge.

Highly recommand to those who have a strong will to learn Deep Learning but without much relevant background. It's definitely possible to catch up with the help of this course and time dedication.

Thanks for providing this awesome course. I love it so much. The dimensions of matrixes used in different layers of neural network always torture me. But this course explains the details including the dimensions very clear and understandable. Also the assignment is well designed and split into reusable modules which you can even use to build your own models.

Andrew does a very good job of explaining the concepts so that it is easy to understand. Also, it is nice that he does not skip any further detailed explanations but instead leaves them as optional to the user. This is quite nice because it gave me the opportunity to delve into the more conceptual items that were not thought as mandatory in the coursework.

This course gave me the intuition behind the neural networks - I didn't really understand the concept of forward/backward propagation before; Andrew did a really good job in describing it with details. Also, I find the focus on vectorization very useful, as it speeds up the code significantly and (in my case) makes it easier to understand what is going on.

Great course! You will learn to implement a neural network with L layers in python. This helps to understand a lot the functioning of a neural network. In addition, you will understand the formal mathematical intuition of the NN. If you are looking to delve into the gradient descent derivation with matrix calculus, you will find good resources in the forum

I loved the course. Andrew is a wonderful teacher! I have a PhD in engineering, have been programming for 15 years, and wrote artificial neural network programs a decade ago. So, this course was a great refresher and helped me come up to speed with "deep learning" (basically multi layer neural networks) and learning how Python can improve execution by 300x.

Very good, overall. Sometimes I feel like "too much" guided, specifically in the programming assigments. Maybe is the way to solve a computer intensive's task like this. With an approach more open the task would be much more difficult, compared to the correct implementation. But I feel happy after finishing the four weeks course!

Thanks everyone at Coursera.

Excellent neural network and deep learning lessons. I had applied neural networks and deep networks on using keras and tensorflow, this course helped me learn the fundamentals behind backpropagation, matrix calculations, dimensional representation, and coding among others. I am thankful and happy that I completed the course. Looking forward to the next one.

The course content and arrangement are wonderful, but the programming assignments have some flaws, i.e., the assignment "Building your deep neural network: Step by Step" in week 4 has a confusing bug in the corresponding TestCase, which causes 0/10 score. In addition, the annotation in the L_model_backward function is wrong which may make students confused.

I found this course very approachable in the way that Andrew Ng taught the course. The course had a very good balance between the video on concepts and programming assignments and quizzes to reinforce the learned concepts. If you're interested jumping into the world of Deep Learning, I would highly recommend this course as a starting step on your journey.

Neural Networks and Deep Learning by Michael Nielsen is an extraordinary course that delivers an in-depth understanding of neural networks and deep learning with clarity and precision. This course stands out for its exceptional blend of theoretical insight and practical application, making it an invaluable resource for both beginners and advanced learners.

I didn't know anything about neural networks or deep learning before taking this course, I only knew that I wanted to learn about it. The content is explained very clearly in each video (even the math). The programming practices were not overly difficult and really help to solidify the material. I truly learned a lot in this course. 10/10 would take again.

The course provided me foundation to understand the basic of deep learning and neural networks. The assignments are well-structured which helped me to better understand the concepts explained in the lectures. I also liked sessions with heroes of deep learning. Their story and advices is really inspiration for someone who want to dive into deep learning.

Curso muy claro, ideal para aprender y familiarizarse con los conceptos iniciales de redes neuronales utilizando Python. Me encantó como están organizados los temas y como usan los ejemplos de los ejercicios de programación para clarificar los conceptos mas importantes. El conocimiento se va construyendo de menos a más de forma ordenada y bien planificada.

I found this a very good course to be introduced to deep learning. The teacher explains in an extremely understandable and clear manner. The only thing I would have wanted to see in this course, is a little more challenge in the labs. Most labs just require you to finish very obvious small parts of code. But I hope I can find that in the following courses.

Pretty good! Wish the code was more individually guided or there were more optional programming assignments/info that focused on how to edit and shape data for input into a NN. It seems like a lot of the coding parts are just hand wavy this works because it does, but it would be nice to see why exactly some parts work especially in the code implementation.

Very good course with high quality course video, materials, assignment. I like the feature that all the course materials can be downloaded instantly and the detailed guidance for python implementation and mathematical background (matrix theory, linear algebra) inside the course for me to have deep understanding what is behind deep learning. Awesome course!

This was really an interesting course which allowed me to build DL models from the ground up. It helps one to better understand what DL frameworks are doing in the background. Such a great learning experience. I would encourage anyone wanting to take this course to spend a week or so brushing up on their python and numpy to be able to move a little faster.

This is a great course. Especially, the teacher is brilliant. I have leaned the key concepts in deep neural network and know how to build a network by myself. The programming exercise is really helpful, where I could deeply understand the structures and connections for each part of deep neural network. The exercise help me better understand how it works!

Excellent course for beginners that want to explore deep learning. The course goes into the fundamentals of Neural Networks, starting with a simple Neuron to build a basic classifier, then using more layers to capture more complex functions, and finally creating deep representations to capture even more complex information from the data.

Highly recommended.

The topic "neural networks" originally spans only 1 week in the instructor's other coursera course "Machine Learning". Here we spend 4 weeks to deep dive into it, comparing logistic regression, shallow nn (2 layers), and deep nn (many layers). It is excellently taught, just like the other course; great coding assignments to deepen students' understanding.