About Andrew Ng's Deeplearning AI courses


Just finished it - would recommend to people who want a bottom up approach for learning deep learning algorithms. It definitely complements Jeremy’s top down approach and helped me to understand couple of “why” questions.

NB - There was supposed to be a miscellaneous thread where I could post this, but couldn’t find it - sorry for that.

(Aditya) #2

Especially creating Conv nets from scratch in Numpy…
Loved it…

(emsart) #3

Totally subscribe to this point of view.

(Justin Ellis) #4

I loved these courses but have been waiting forever for the last part on Sequence models. I took those courses first before coming to fast.ai. To me, I like that progression as Andrew Ng does a great job on concepts and basics but implementation of real code is missing whereas fast.ai is amazing at showing how deep learning experts actually do things.

(emsart) #5

I’m waiting for the Sequence models course too ! I agree with the implementation of real code, even though, from what I see so far (only on week 2) I’d love to have a detailed of fast.ai code, how it calls pytorch etc.


Well you can ask your question out here - https://discuss.pytorch.org/ . And for detailed explanation, you can ask people in this forum as well.

(Aditya) #7

I wonder how many want Jeremy To Teach Python??

I am first one who will be glad if Jeremy creates a lecture or two on Python(tricks, OOPs etc…)

Especially the way he taught the OOPs concepts,(lec 6,7 ML)

I can bet that no book I know of did it that way…

You are Awesome…


Tagged Jeremy.



May be tag Jeremy here - he might respond to your request.

(Jeremy Howard) #9

I’m here :slight_smile: It’s an interesting suggestion… I’m really glad you liked the mini OOP thingy


Can you share link to that courses? Thanks!


https://www.coursera.org/specializations/deep-learning - its a four course series.


All the lessons (4 courses) are also available on youtube.
Deep Learning .AI

(damien ng) #13

@mmr Could you give some examples of “why” question you mentioned in the original post? Thanks.


Well there were couple of things explained in much granular format using numpy -
e.g - As someone already mentioned inner working of convolutional network in numpy.
I was interested in knowing how some of the weight update algorithm works - RMSProp , Adam, weight update using momentum.My favorite was actually face detection and face recognition using conv net - it was an application of one shot learning.
But I still have to maintain that take the best of both of the two worlds - lessons from fast.ai Part 2 - as well as deeplearning ai - you are good to go in deep learning.(…thats my subjective view)


What I found that , if you have done the courses here, you should be good to read scientific papers and implement then yourself without much effort. There was not a ton of value that I got from Coursera. I found that the assignments were too trivial and to much was set up for you. There were one or two things that I liked, like some things in pure numpy. If you do want completeness you can read the deeplearningbook and watch the lectures for that. It’s covered on a chapter basis.

(Aditya) #16

But scientific papers go into mathematics always…?
How to overcome that?

We can now implement a model as shown in the architecture but what they add to it makes it difficult to complete the paper implementation…

Things whizz pass my head just like a Bullet..


Most of the math in papers is just difficult notation but logical easy to grasp.
I still dont understand a paper 100% the first time i read it, but after a few times it gets fairly easy to grasp

(Mohamed Ali) #18

Couldn’t agree more. The two courses work great together specially for beginners.