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What are some good resources for learning about DSP (including the mathematics and algorithms necessary for actually understanding these resources)?
Let's assume that my math skills are rusty from lack of use as well, so a roadmap along the lines of:
Stats refresher
Calculus refresher
Solid newbie explanation of FFT
(50 steps later...)
would be nice and hopefully result in DSP skills and knowledge approaching "competent".
How Do I Learn DSP?
A Beginner's Guide to Digital Signal Processing
As well as the The Scientist and Engineer's Guide to Digital Signal Processing
By Steven W. Smith, there is also the excellent Understanding Digital Signal Processing
I learned a lot from the Scientist and Engineer's Guide to DSP. You can read it for free online at http://www.dspguide.com/ It's nice because it focuses more on what you can do with DSP, rather than the underlying math.
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I am trying to do a paper about the Machine learning been applied in NLP. Can you guys please suggest me applications that have already used the Machine learning with the NLP?
The list is broad since machine learning is becoming more and more mainstream.
Regarding text, images and video, a good list of APIs would be:
AT&T Speech, IBM Watson, Google Prediction, Wit.ai, AlchemyAPI, Diffbot and I guess Project Oxford as well.
Hope it helps.
If you want something generic you can use this tutorial: http://www.cs.columbia.edu/~mcollins/papers/tutorial_colt.pdf
It is probably not the more recent information but you could find it useful if you start to learn ML methods for NLP.
As it is mentionned in this tutorial, ML methods are generally linked to the NLP task (Information Extraction, Machine Translation, etc.).
IBM Watson project is an example of platform that uses NLP and ML.
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I am keen in learning machining learning. I know programming, just want to know some useful sites which will help in understanding the concepts of machine learning with simple examples.
As a beginner in Machine Learning you should start with the book
Pattern Recognition and Machine Learning, by Christopher M. Bishop
There are some prerequisites other than programming are Linear Algebra, Probability theory, etc. i.e. you should have a strong background in Mathematics. Although the book I suggested covers the common mathematical frameworks needed for understanding Machine Learning in its introductory chapters.
Moreover, you should practice implementing different learning algorithms (start from smaller ones) to grab the concepts well. As Andrej Karpathy said,
...everything became much clearer when I started writing code.
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I'm a recent grad with a CSS degree and I never had a chance to take a course on distributed systems, but have progressively become interested in the topic. I would love to dive into the subject head-first in hopes of beginning my career as a software developer in that field. I've taken an Operating Systems course and have knowledge of multithreaded programming, along with elementary knowledge of distributed systems concepts, but that's about as close as I got to the subject, which isn't close at all.
Does anyone know a good place to start learning about the subject for someone with a CSS degree?
Do I really need a strong background in distributed systems specifically to get an entry-level job or do you think there are companies willing to hire people with strong programming skills but not necessarily strong knowledge of distributed systems?
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Disclaimer: although I know some things about big data and am currently learning some other things about machine learning, the specific area that I wish to study is vague, or at least appears vague to me now. I'll do my best to describe it, but this question could still be categorised as too vague or not really a question. Hopefully, I'll be able to reword it more precisely once I get a reaction.
So,
I have some experience with Hadoop and the Hadoop stack (gained via using CDH), and I'm reading a book about Mahout, which is a collection of machine learning libraries. I also think I know enough statistics to be able to comprehend the math behind the machine learning algorithms, and I have some experience with R.
My ultimate goal is making a setup that would make trading predictions and deal with financial data in real time.
I wonder if there're any materials that I can further read to help me understand ways of managing that problem; books, video tutorials and exercises with example datasets are all welcome.
Take ML course on coursera. It is a good introductery into ML algorithms which will tell you what ML could do\some general approaches:
https://www.coursera.org/course/ml
Also to get a broader picture I suggest coursera's DataSciense course:
https://www.coursera.org/course/datasci
Finally a good book is Mahout in action - it is more about solving practical matters with mahout and has lots of examples and case-studies.
I beleive after that you will have a better understanding of what you want to do next.
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I was curious if anyone knew of something like this flowchart but for Computer Vision tasks? Specifically for OpenCV would be most ideal.
Or any references with best practices, and common patterns for Computer Vision problems?
That's a monumental task. The best I could find is from this article and it's a little bit old:
Maybe it's a good time to commit to FlexCV on Kickstarter.com, a GUI for OpenCV that allows you to create complex algorithms in a matter of minutes by connecting graphical elements together. It's an alternative for Adaptive Vision, but purely based on OpenCV features.