Deep-Fried Data
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Deep-Fried Data
1–10 of 149 posts
Re: Deep-Fried Data
#2Reminds me of the phrase "graduate student descent" for training neural networks...
I've been noticing more casual dismissiveness towards grad students lately. They are certainly often treated as the grunt laborers of academia, in areas where career prospects are downright stupid. I generally feel it would be more productive to at least pretend that they're being trained to be independent, aggressive researchers in their own right, though.
Re: Deep-Fried Data
#3Um, I'm sorry, but unsupervised learning and deep learning are not the same.
Re: Deep-Fried Data
#4"...Dim witted grad student that you can't really trust..." Reminds me of the phrase "graduate student descent" for training neural networks... I've been noticing more casual dismissiveness towards grad students lately. They are certainly often treated as the grunt laborers of academia, in areas where career prospects are downright stupid. I generally feel it would be more productive to at least pretend that they're…
Re: Deep-Fried Data
#5"The names keep changing—it used to be unsupervised learning, now it’s called big data or deep learning or AI" Um, I'm sorry, but unsupervised learning and deep learning are not the same.
Re: Deep-Fried Data
#6The author's concerns over machine learning are well-founded. The best option I've been able to identify to ameliorate some of the concerns is focusing on the population that will be suppressed. Once the model returns the desired recall / precision, drawing samples from the excluded population with a rigorous acceptance standard can help validate whether you've simply built a model around your biases. Couple that with allowing an opponent to validate a randomly-selected sample and you've cleared up a lot of the uncertainty in the model.
It's not perfection, but perfection is a very difficult standard.
Re: Deep-Fried Data
#7"The names keep changing—it used to be unsupervised learning, now it’s called big data or deep learning or AI" Um, I'm sorry, but unsupervised learning and deep learning are not the same.
What's the distinction?
Deep learning refers to a particular type of a particular learning technique: Specifically a neural network that has many hidden (intermediate) layers. Deep learning can be used for either supervised or unsupervised learning.
Re: Deep-Fried Data
#8Re: Deep-Fried Data
#9Rather hilariously, deep frying is already a term of art in ML, of course in a radically different setting. Deep fried convnets ( https://arxiv.org/abs/1412.7149 ).
Re: Deep-Fried Data
#10Earlier quoted context omitted.
What's the distinction?
Unsupervised refers to whether or not the dataset is being trained against anything. Think about the difference between: How many people will view this webpage? Divide these pages into 20 clusters? The first is supervised. The second isn't. Deep learning refers to a particular type of a particular learning technique: Specifically a neural network that has many hidden (intermediate) layers. Deep learning can be used f…