What order should I take your courses in?
This is THE most common question I get asked. Machine learning / deep learning / data science is a huge field and you will spend many years learning. In fact, you will probably never stop learning. Thus, it's important to understand the difference between the stuff you should learn right now vs. the stuff you'll be studying one year from now. Some courses are prerequisites to others, which are in turn prerequisites to others. To map out these dependencies, I created this visualization: click here to see it
How can I contact you?
Email me any time at info [at] deeplearningcourses [d0t] com.
Why is the material so hard!?
The NUMBER ONE reason I see students fail is because they do not pay attention to the prerequisites. If I say you need to know calculus to understand a course, it's because you need to know calculus to understand that course. You can't go from knowing nothing about machine learning to understanding word2vec in one day. You should check out the course ordering to understand how the prerequisites are structured.
Why is this website so fast!?
Why is there so much math!?
Sorry to burst your bubble, but machine learning pretty much is math.
If all your courses depend on other courses why didn't you make it one big course?
The courses are distinguished by topic and by a skill-based separation. You will notice that the more advanced courses move much more quickly and contain less hand-holding. Why? Because all the required skills are taught in the prerequisites to those courses. You are not meant to just watch video for 10 hours straight. You want to spend at least a week, perhaps a month even, making sure you can independently do all the derivations and write all the code. By doing this, you prepare yourself for later courses.
I'm confused. Can I ask you a question?
Yes. Below each video lecture there is a discussion board. Please ask your question there.
How do I know if I've "passed" a course?
Since this is not traditional school, you will receive no grades. Machine learning also doesn't really have grade-school type homework questions like the kind you would find in a physics textbook. You want to make sure that (1) you can do all the mathematical derivations yourself without any reference, (2) you can code all of the algorithms yourself without any reference. It's ok if your code looks different from mine. The beauty of machine learning is that when you run it on data, it either works, or it doesn't.
Help! The videos are not working for me! I get an error like "Either the server failed or the media is not supported".
Some users who are behind a firewall or whose work networks use a firewall have reported difficulties viewing the videos. Click on the button that says "Help! No video!" at the lower left-hand corner of the video. You may either contact us directly to ask for help, or alternatively click the link that allows you to change to an alternative video source.
If I purchase a course on Udemy, can I get it here for free or vice versa?
Unfortunately no. The courses on deeplearningcourses.com are generally the "VIP versions" and contain more material. Therefore, it would not be fair to the other students. It is also not possible to access the course for free on Udemy if you purchase it here, due to limitations of Udemy's coupon system. There are caveats to this; for instance, if you purchased the "VIP version" on Udemy. Read more here: How do VIP courses work? For courses you purchased elsewhere and want to transfer them here for free (assuming that they meet the criteria in the aforementioned link), there will be a limit of 2 courses per year that can be transferred.