Google's Free Deep Learning Course

It's a course assuming some background in ML.

For _probability_ vectors, there is a distance measure called cross-entropy. It's a standard error measure in classification problems. It has some important properties different from the Euclidean distance (especially for low probabilities) and there is information-theoretic interpretation.

More on it:

- http://stats.stackexchange.com/questions/80967/qualitively-w...

Euclidean distance is not a good measure in higher dimension: http://stats.stackexchange.com/questions/29627/euclidean-dis...

Because he wrote one of the most successful ones?

To be clear, the posted course is not a survey course in machine learning. It is instead a more practical course on using TensorFlow to build deep neural network architectures useful for certain tasks.

The link the OP posted is a (great) survey course dedicated to machine learning as a whole, which includes methods other than deep learning.

I was just beginning to give it a try, it just requires you to type the code that is shown on video. Poor way of teaching something, it seems at first. I sense this course is just to teach me the tools of the trade, not really enabling students to fully understand what they're doing.

On the other hand, some months ago I watched the ML course by Andrew Ng, and I still did not understand how to test a simple linear regression for myself, so I did not really understand it, and stopped watching the course.

I would start with the nano degree program if you don't have any prior knowledge of ML.

Udacity has a Linear Algebra review course, but I don't believe it is public for now. I had taken a linear algebra course before I took the GT ML class, but I wasn't a expert by any means. I don't believe you will need a deep understanding of linear algebra before taking this class. Singular value decomposition might come up. I think if you are familiar with everything in the following pdf you should be fine.

http://minireference.com/static/tutorials/linear_algebra_in_...

If you are motivated, you will do fine. Good luck!

Simple explanation of the basics:

http://www.intmath.com/matrices-determinants/matrix-determin... - matrices

http://www.intmath.com/vectors/vectors-intro.php - vectors

Linear Algebra using Python:

https://www.coursera.org/course/matrix - Coursera: Coding the Matrix

http://codingthematrix.com/ - Website: Coding the Matrix

The simplest software for linear algebra would be GeoGebra, http://www.geogebra.org/. For instance, to enter a matrix just have the spreadsheet view open, enter the numbers, highlight the cells, then choose the option "Create Matrix". To enter a vector start writing "vec" in the input bar at the bottom and intellisense gives you the option to choose "Vector[<Start Point>, <End Point>]". Choose this. Fill it in, for example, "Vector[(-3, 4), (1, 2)]" (Hint: Use Tab to move between options in the input formula, here to move between "<Start Point>" and "<End Point>]".) Voila the vector is drawn! You can even draw a vector with just two clicks in Graphics view, if you first select the "Vectors" tab at the top (the symbol is a line with an arrowhead). The GeoGebra software is really incredible for learning/doing Linear Algebra, Calculus and Statistics. A real godsend.

Linear algebra is simple (but ubiquitous - so you need to feel it, not only "sort of follow it").

Some visual matrix operations are here: http://setosa.io/ev/ If you want a beginner textbook, I recommend http://www.matrixanalysis.com/

Again, it is simple, so maybe you can even take course and look up Wikipedia when needed (but for me it is hard to guess you level, current knowledge, etc).

In any case, this ML course assumes some ML knowledge.

Haven taken this course (though I will), but check out Andrew Ng's Coursera's ML course. He gives a crash course in linear algebra. It helped me a lot in my endeavours in ML & neural nets. I also like to learn as I go along, and this intro was just about enough to get started.

I believe all the courses on udacity are free. They make money through offering nanodegrees, partnering with companies, and a partnership with Georgia Tech to offer an online master's degree in computer science.

So free to take, money if you want degrees.

Yes it does. I wonder if it's overkill but Google has to push tensorflow to the market some how

From the site: "Complete learning systems in TensorFlow will be introduced via projects and assignments."

Likely. Data analysis (of which ML is an important part) is needed in many places, from entry-level to top-level.

I am a data science freelancer and I mostly do projects for IT-dominated companies. First, I was surprised that such companies need some external help with relatively simple tasks; only later I discovered that top-notch performance in webdev (or even: algorithms) does not mean that someone is able to do simplest data analysis.

For data science / ML - I know a lot of openings in which they are looking for "data scientists", but what they mean is software engineers with at least a slight idea what is data analysis.

When it comes to deep learning in particular - I don't know.

It's enough tasks where you need to have understanding of the ML algorithms/workflows/tools, and be able to implement production system that integrates them into real systems, generating value for companies. In many cases you need to have very good domain knowledge & software development skills in addition to understanding of ML. And in ML-related systems, the big part of implementation not ML itself, but a lot of supporting stuff (figure 1 from "Hidden Technical Debt in Machine Learning Systems" paper (https://papers.nips.cc/paper/5656-hidden-technical-debt-in-m...) quite useful for understanding).

I personally took several ML courses from coursera/udacity/edX, and they helped me when I decided to move to another group that works on the ML-related projects.

No. Honestly, no. Do it because you think it's interesting. Very few companies do deep learning (Google, Facebook, and Microsoft come to mind - it might be useful if you work at these companies already). That number will undoubtedly grow, but the vast majority of ML/data science positions deal with stuff like linear regression, PCA, logistic regression, decision trees, random forests, maybe svms/boosting if you want to get fancy. Take an ML survey course like Andrew Ng's course to learn about these. Also basic statistics/probability.

It sort of makes you wonder if we're looking at a future where something like 5 teams composed of 1000 of the top researchers each are going to build the premier ML systems, and those teams can then solve most generalized ML tasks.

Most other programmers wouldn't be able to contribute much value in a world that worked like this.

This perhaps mirrors how the chip market works, which similarly involves a limited number of researchers involved in advanced manufacturing techniques that are highly specialized and mostly a mystery to other people in the technology field.

Machine learning only going to be more and more relevant in the tech industry. Eventually you're going to have to deal with some sort of data analysis, just because there is little to no barrier from data collection to data analysis.

I'm not sure that a deep learning course would be a good first course. But an intro course on linear regression and basic probability/statistics would be worth looking into.

I'd recommend really mastering basic statistics if you aren't going to go all the way with learning data analysis. It's surprisingly subtle and more widely applicable to a broad range of careers.

If you can make the time than learning new things and taking courses is always a good idea. You never know where you're going to end up as a developer. Who knows you may end up changing the course of your career. Also machine learning and AI are all becoming big fields.