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This is very similar to Andrew Ng's Deep Learning Specialization course . If you found this blog post enjoyable be sure to check it out. It is a great course and the intuitions behind NNs are explained very clearly.
Andrew Ng shares his impressive knowledge on Coursera (which he is a co-founder of). For those interested:
- https://www.coursera.org/learn/machine-learning (Machine Learning Course)
- https://www.coursera.org/specializations/deep-learning (Deep Learning Specialization)
⬐ madeuptempacctI didn't find the machine-learning course to be that great, but I don't know anything about machine learning.
I thought both of these two courses on coursera were quite good:
First one is a bit older school, but takes you through all the fundamentals and actually explains a lot of the math involved. It also gets you thinking a lot more about how to solve problems from a Linear Algebra standpoint and the types of problems machine learning is good for tackling.
Second one is a much more modern day set of courses specifically focused on Deep Learning techniques and problem solving.
I thought both were great. First one is free as well...
I'd suggest starting with this excellent course:
And then dive into competitions on kaggle.com.
Then following up with the deeplearning.ai specialization here: https://www.coursera.org/specializations/deep-learning
and the http://www.fast.ai/ courses.
You'll be up to speed in no time :)
I totally agree with you.
I started Deep Learning Specialization in Coursera: https://www.coursera.org/specializations/deep-learning last month and almost finished it, but I realized this field requires a lot of expertise, not something you can learn in a month. What I learned in the courses was just a basic topics in Deep Learning and how to use Numpy, TensorFlow and Keras.
I was considering diving into a Data Science job and started that specialization as a starting point, but I just realized how foolish I am. Chances are I'll find a job, but it definitely takes another 10 years/10,000 hours to master this discipline.
Anyway, the specialization is wonderful and Dr.Ng explains complicated Deep learning topics in a way that is understandable for everyone. So if just learning is what you want, you should take it, but I don't think you are prepared for a real world Data Science job after finishing it.
Here's another resource that I've been following the past few weeks. Andrew Ng recently launched a Deep Learning Specialization (Understanding of an into to ML course is a prerequisite) under deeplearning.ai , and I really enjoy the content so far.
The basic mechanisms for building a neural network from scratch are almost disappointingly simple (provided you know a little bit of calculus and linear algebra). And setting up a basic network in an existing architecture is pretty trivial.
I'm currently busy with the neural networks and deep learning specialization on Coursera.
The trick, as far as I can tell, lies in with the various techniques for setting up your data, tuning your hyperparameters, and picking the right architecture for the job. At least, this seems to be the message of the course. It seems to still be a bit of an ad-hoc field. There are a number of techniques and things to try, without there necessarily being more than a shallow theoretical understanding from the experts as to why they actually work.
Then, of course, there are the experts and researchers who come up with entirely new architectures. Now that actually takes skill.
⬐ hoopladlerIsn't this literally the point of computers? A processor is something that does amazingly stupid stuff 1.3 million times a second.
If you do even very simple things rapidly enough, you can get amazing results.
I took the coursera specialisation one week ago. It takes you from the very basics to some more complex modules like keras or tensorflow. If you are into it and have time, the whole 4 courses can be done in the free week: https://www.coursera.org/specializations/deep-learning
I was at the same point as you until I discovered the new Andrew Ng course on deep learning 
It's a good structured way to learn the core of ML while learning about Neural Networks and without having to become and linear algebra expert which for most people including like me was a deal breaker with other courses. The timing is great too as ML now is so much different than it was 2-3 years ago.
https://www.coursera.org/specializations/deep-learning by Andrew Ng is a great resource for anybody who wants to learn neural networks. It pretty much steps you through all the issues raised here and much more.
⬐ elemarGreat suggestion! I find it very enlightening too and to be honest I actually attend his courses :)
You can also dive in first and then cover the math behind ML, by taking Andrew Ng's courses. https://www.coursera.org/learn/machine-learning https://www.coursera.org/specializations/deep-learning
At first I could not find a way to sign up without paying - when I click enroll, I didn't see the audit link that Sjenk and others mention.
The trick is that the audit link only appears when you sign up for the individual course, not the entire sequence. So if you go to this link:
... and click "Enroll", you can only proceed by supplying payment info. However, if you scroll down to that page to the box titled "Course 1", at the bottom of that box is a link "You can choose to take this course only. Learn More".
Click on THAT to go to the individual course page. Then, click Enroll, and in the first box that pops up, you'll see the link "Or audit this course" in the lower left.
This allowed me to sign up for all five without supplying payment info.
⬐ ToraiBut you can't submit exercises.
When you audit a course:
- You'll be able to see most of the course materials for free, but you won't be able to submit certain assignments or get grades for your work.
- You won't be able to submit assignments for feedback or a grade.
- You won't get a Course Certificate.⬐ Blackthorn⬐ NelsonMinarUgh, Coursera has gone way downhill.⬐ sgsloIts a business, do you not expect them to somehow collect revenue on products they have created?⬐ ghaffThe basic problem is that Coursera wasn't successful in attaching some meaningful value to their certificates as credentials. And, if the credential isn't meaningful, why on earth would I want to pay a VC-funded company for a PDF that has zero value to me? Taking the course may be worthwhile but a certificate adds essentially nothing to that.
So now they've effectively eliminated just about the only thing that distinguishes them from some YouTube videos and a textbook.⬐ gaius⬐ BlackthornThe basic problem is that Coursera wasn't successful in attaching some meaningful value to their certificates as credentials.
The EdX solution to this is that their courses are all endorsed or run by brick-and-mortar universities with considerable investments in their brand that they won't want to tarnish by attaching it to any random certificate.⬐ ghaffI'm not sure to what degree EdX has really "solved" this. My impression is there's quite of range of quality and rigor on EdX as well. And, more centrally, most people still don't see EdX certificates as general substitutes for more conventional educational degrees.⬐ gaiusNot a degree sure but many e.g. XSeries and MicroMasters are at the level of diplomas.They've created the courses? Seems to me like it's the professors teaching them that created them. What value is Coursera adding?⬐ posterboywhen they start for free, there's hope they aren't just trying to beat the competition with discount prices.I wish I could figure out what the actual price is. I'm logged in and all I see is "Enroll free!". The only pricing disclosed is $49/month. Courses 1-3 are 9 weeks, so does that mean it costs about $100? (No lengths on courses 4 and 5 yet.)⬐ chairmankagaTo add to that, I couldn't make it work following these steps, but logging out and following the steps above worked for me. Thanks.
Here is the link to the coursera web-page: https://www.coursera.org/specializations/deep-learning
In-short this specialization covers:
2.Hyperparameter tuning, Regularization and Optimization
3.Structuring Machine Learning Projects
4.Convolutional Neural Networks