Supervised Learning Quotes

Take any old classification problem where you have a lot of data, and it's going to be solved by deep learning. There's going to be thousands of applications of deep learning.

Pretend I asked, now answer the question.

Once your computer is pretending to be a neural net, you get it to be able to do a particular task by just showing it a whole lot of examples.

The paradigm shift of the ImageNet thinking is that while a lot of people are paying attention to models, let's pay attention to data. Data will redefine how we think about models.

The thing that's going to make artificial intelligence so powerful is its ability to learn, and the way AI learns is to look at human culture.

When you have a large amount of data that is labeled so a computer knows what it means, and you have a large amount of computing power, and you're trying to find patterns in that data, we've found that deep learning is unbeatable.

Innately, children seem to have little true realistic anxiety. They will run along the brink of water, climb on the window sill, play with sharp objects and with fire, in short, do everything that is bound to damage them and to worry those in charge of them, that is wholly the result of education; for they cannot be allowed to make the instructive experiences themselves.

You have to teach your algorithm what it can do and what it cannot do because, otherwise, there is a risk that the algorithms will learn the tricks of the old cartels.

Machine learning is the science of getting computers to learn without being explicitly programmed.

Want to train a machine translation system? Train it on a gazillion pairs of sentences of parallel corpora, and that creates a lot of breakthrough results. Increasingly, I'm seeing results on small data where you want to try to take in results even if you have 1,000 images.

Deep learning is a subfield of machine learning, which is a vibrant research area in artificial intelligence, or AI.

The revolution in deep nets has been very profound. It definitely surprised me, even though I was sitting right there.

Beyond helping other people build AI systems with Deeplearning.ai, I also hope to build some AI systems myself!

The deep learning techniques, while relatively easy to learn, are quite foreign to traditional engineering modalities. It takes a different mindset and a relaxation of the presumption of control. The practitioners are like magi, sequestered from the rest of a typical engineering process.

With the Google Brain project, we made the decision to build deep learning processes on top of Google's existing infrastructure.

In deep learning, the algorithms we use now are versions of the algorithms we were developing in the 1980s, the 1990s. People were very optimistic about them, but it turns out they didn't work too well.

As commentators like the American psychologist Gary Marcus have noted, it's extremely difficult to teach a computer to recognise cats. And that's not for want of trying.

We now think of internal representation as great big vectors, and we do not think of logic as the paradigm for how to get things to work. We just think you can have these great big neural nets that learn, and so, instead of programming, you are just going to get them to learn everything.

I think the first wave of deep learning progress was mainly big companies with a ton of data training very large neural networks, right? So if you want to build a speech recognition system, train it on 100,000 hours of data.

Graphics has lately made a great shift towards machine learning, which itself is about understanding data.

AIs are only as good as the data they are trained on.

All you need is lots and lots of data and lots of information about what the right answer is, and you'll be able to train a big neural net to do what you want.

The neural network is this kind of technology that is not an algorithm, it is a network that has weights on it, and you can adjust the weights so that it learns. You teach it through trials.