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Neural Networks and Deep Learning

Coursera · deeplearning · 7 HN comments

ai. If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new ...
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This course is offered by deeplearning on the Coursera platform.
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this one... https://www.coursera.org/learn/neural-networks-deep-learning...
I highly recommend Andrew Ng's Coursera courses for both Machine Learning and Deep Learning. Good for beginners, Math is taught along with the course, and gets you a solid foundation:

https://www.coursera.org/learn/machine-learning/

https://www.coursera.org/learn/neural-networks-deep-learning...

dagw
Andrew Ng's courses are excellent. Another pretty good Coursera course is Machine Learning Foundations from the University of Washington. It is very high level and novice friendly. While it covers non of the math and very little programming it does give a nice quick introduction to the most popular ML techniques out there and when to use them. It all depends on what level you are interested in starting. They also have follow up courses that go deeper into the different techniques.
ravishk
+1 for this recommendation.

I would specifically recommend Machine Learning Foundations: A Case Study Approach - It is fantastic and helped me greatly start my ML journey last year.

Turi is awesome, I hope Apple is doing something great with it.

hackathonguy
Thank you! Should I start with the Machine Learning one?
fargo
I would recommend starting with deep learning first since that's what you are interested in and it covers all the ML principles you need to be familiar with. If you want to go deeper and get familiar with other ML techniques too you can easily follow the old course afterwards.
icc97
From my experience Andrew Ng wiped the floor with every other lecturer I've had. Both the ML and his new Deep Learning course.

If the lecturers aren't very interesting Coursera can be as hard as any other lectures. I gave up on the Scala functional programming and disappointingly have stalled with Geoffrey Hinton's Neural Networks courses.

But I really can't understate how good Andrew Ng is, he has a very relaxed manner and manages to make some very complex topics seem almost trivial.

The worst of the mathematics is derivatives and matrix multiplication. You can even avoid matrix multiplication mostly in the ML course, but in his Deep Learning course he takes you through the 300x performance benefit you get from using NumPy and matrix multiplication vs loops.

binarymax
At your level yes, I would recommend starting with the ML course. It is really beneficial to understanding how the mathematics work.

The two most important things to remember, since the courses are challenging: 1) don't be in a hurry, and 2) don't give up! Take the time to learn every detail presented, do the optional exercises, and dig deep.

xeromal
It's definitely challenging. The math and just seeing the complicated formulas really push me, but the reward is good too. I'm tired of pushing pixels and doing some meaty stuffy like ML is a nice change of pace.
You may want to check out Andrew Ng's Deep Learning Specialization over on Coursera. [1] One of the courses is specifically about hyperparameter tuning and another about structuring your project. There is a lot of practical information scattered across all the courses.

Yes, I'm taking the specialization and having a blast with it. :-)

[1] https://www.coursera.org/learn/neural-networks-deep-learning

There are several courses in this specialization. Only 3 are currently available for enrollment right now.

You can get the course material for free, but won't be able to get assignments graded.

Below are links for all of the courses.

Course 1: https://www.coursera.org/learn/neural-networks-deep-learning

Course 2: https://www.coursera.org/learn/deep-neural-network

Course 3: https://www.coursera.org/learn/machine-learning-projects

Course 4: https://www.coursera.org/learn/convolutional-neural-networks

Course 5: https://www.coursera.org/learn/nlp-sequence-models

To get the course material, you go to each course link and click on "Enroll". Then look for the "Audit" link at the bottom left of the modal dialog that comes up.

If you click the Enroll button on this link you will find the Audit link: https://www.coursera.org/learn/neural-networks-deep-learning
Many weeks end with an interview that might be interesting independent of the course -- the first is a long one with Hinton.

https://www.coursera.org/learn/neural-networks-deep-learning...

For those who are unfamiliar with coursera or interested in just the videos (and NO certificate) you can enroll in "AUDIT" mode:

AUDIT MODE: http://image.ibb.co/iwm0xF/ng.png

The deep-learning course consist of 5 subcourses:

https://www.coursera.org/learn/neural-networks-deep-learning

https://www.coursera.org/learn/deep-neural-network

https://www.coursera.org/learn/machine-learning-projects

https://www.coursera.org/learn/convolutional-neural-networks

https://www.coursera.org/learn/nlp-sequence-models

The deep-learning course is a different course than the prerequisite machine-learning course:

https://www.coursera.org/learn/machine-learning

flor1s
Thanks for the links! It seems like you can already browse the lecture videos of the first three courses!
smithsmith
Can the certificate be received by completing the course in audit mode and then paying at the end of the course.
ryanschneider
BTW, if you want to audit, you need to search for each course individually (or click seycombi's links above) and click the Enroll button there, there's no Audit option if you click Enroll on the full Specialization.
jjuel
Thank you for pointing this out! Was looking for the way to just audit it and could not figure it out.
forgotmysn
fast.ai also has a quality ML course
supermdguy
I love fast.ai's course, but I also appreciate having a solid and thorough free machine learning course. While fast.ai is good for getting onto the cutting edge of deep learning quickly, it doesn't go through a lot of material that most people in the machine learning industry are assumes to know.
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