If you're trying to learn about deep learning, I highly suggest using Python(Theano) or Lua(Torch). They're free and used by the experts in the field for research.
Even if you don't want to use the frameworks, you'll still have access to fast linear algebra routines.
That's right; we can't know for sure and I'm sure you'll get some backlash for this comment. As with everything though, we need a balance.
If we say that we can't know for sure and give up, then no advancements would ever be made in any field. On the other hand, we should recognize that science does not claim to provide us with certainty. Many of us speaking "objectively" with scientific "facts" often become emotional and arrogant when speaking to anyone who doesn't agree with our conclusions.
I can say pretty certainly that all the text they've gathered through the Google Books project is in use in their language models and other AI models for their search engine, speech recognition, etc.
They got what they wanted. I can't see what incentive they have as a business to grant access to the books that justifies paying employees for it.
I've found reading the right books to be the best way. Lectures tend to be too broad for me, although they can serve as good overviews.
My philosophy is that if I want to learn something, I get a few suggestions for books, briefly skim through their contents and pick one. After that I'll take notes in it and reread and basically squeeze every ounce of knowledge that I possibly can from it until it's engrained in my brain.
If you're a beginner, you're probably going to be too overwhelmed by the options. I often find emailing/asking a few different professors/researchers/students in the field you want to learn for suggestions more productive.
That's not to say this isn't helpful. This is from my own personal experience.
I am somewhat ignorant. What significant achievements does Dawkins hold in his field? All I know is that he's known for trying to convince others of his beliefs.
A quote from someone who commented on the page:
"When the programming addicts on Hacker News write about programming addiction, they almost never call it programming addiction. Usually they call it being passionate about programming. And in their opinion, programmers who are not passionate about programming are bad programmers."
Not saying I agree, but it could be the start of an interesting discussion.
"Why not just build M with neural nets from the beginning? Without the right data, neural nets couldn’t provide a service much more powerful than, well, Siri, and Wit.ai’s tech can get things started with relatively little data. “This is a good way to bootstrap. With a few thousand data-points, you can start to build a model,” Lebrun says. “Then, using this model, you get more data, and once you have about a million data points, you go to Yann and get some deep learning.”" [1]
I'm not much of a mathematician, but I found "Mathematics: The Loss of Certainty" by Morris Kline (http://www.amazon.com/Mathematics-Loss-Certainty-Oxford-Pape...) very insightful in regards to the development and current state of mathematics. A brief synopsis:
From Amazon:
"This work stresses the illogical manner in which mathematics has developed, the question of applied mathematics as against 'pure' mathematics, and the challenges to the consistency of mathematics' logical structure that have occurred in the twentieth century."
From goodreads.com:
"Most intelligent people today still believe that mathematics is a body of unshakable truths about the physical world and that mathematical reasoning is exact and infallible. Mathematics: The Loss of Certainty refutes that myth."
Edit: This was also interesting: https://www.youtube.com/watch?v=RlMMeqO7wOI , a video by Stephen Wolfram. I know he is often criticized for various reasons, but much of what he says makes intuitive sense.