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Everyone's into deep learning, but what would I actually do with it? With some other field, like computer graphics, one can fairly quickly get a 3D cube spinnin

by nsxwolf 10y ago

Everyone's into deep learning, but what would I actually do with it? With some other field, like computer graphics, one can fairly quickly get a 3D cube spinning on their screen and know it has some relation to the special effects in the Star Wars movie they just saw. No one makes it obvious what the hobbyist can expect to do with deep learning or how it relates to the broader world.

malux85 10y ago
Everyones into Graphics programming, but what would I actually do with it? The uses are myriad, you can build Star Wars Movies, you can build computer games. You can do CAD and 3D modelling and printing, you can visualize fluid simulations But each is an entire field. The same with Deep Learning - You can do image classification for medical diagnosis. Self Driving Cars. Realtime Translation. OCR. You can model chemical reactions by doing latent space exploration. You can model gene-gene interactions. You can build a image recogniser to tell if your cat is on the couch. You can build an AI that plays GO, or any other system or game where the rewards are time-delayed and sparse. You can make it play Atari games. You can stitch satellite images together. You can reconstruct parts of photos that are missing. You can colour black and white images. You can de-noise wind sounds from microphones. You can search for comets. You can monitor deforestation. You can count cars in car parks. You can search vast ocean areas for survivors. You can build security drones that fly around at night and look for anomalies. You can turn a webpage of unstructured text into structured query-able forms (see named entity recognition). You can create visual art (google: neural style transfer) These are just a small drop of some of the cool things that are going on - and while it might be difficult for you to advance state of the art without a GPU cluster (just as it would be difficult to advance state of the art in Graphics programming) the hobbiest can certainly start in any of these fields, just pick one that inspires you.] EDIT: I run a Deep Learning startup - if you want some pointers or help getting started, or would like advice don't hesitate to email me - it's in my profile :)
- gopher_protocol 10y ago
  One of my partner's tasks as a legal assistant is to go through mountains of OCRed PDFs and classify them and extract pieces of data so that lawyers and paralegals can go through them more easily. Do you imagine deep learning would be an appropriate means of automating that, or is it overkill?
  - malux85 10y ago
    No not overkill at all - I think the first step is to augment the human so the machine helps them, then it can totally replace all of the laborious stuff as time passes. Imagine something like: A deep network reduces a 20 page document to a summary of 4 or 5 sentences, you can click on these sentences to "expand" them out, eventually getting to the original text. Saving them from reading the whole document A separate classifier automatically classifies the document into one of say, 20 categories (or whatever is appropriate). A Deep Learning named entity recogniser extracts the Human names, Dates and times, Email addresses, Company Names, email addresses, Money amounts, and numbers from each document, then off to elasticsearch for indexing and easy searching. Then we can start to play with higher level legal concepts that (for example) set precedent, or search for certain logical fallacies. (the next step past machine learning is machine reasoning - and it's starting to be possible now)
  - martingoodson 10y ago
    I have built deep learning systems for similar problem domains. I am happy to chat to you about your example. Email me if you get a chance.
  - webmaven 10y ago
    Not overkill at all, but youy should know that there are a lot of competitors in the e-discovery software category, and they do a lot besides classification, starting from the ingestion of email archives, through extraction of attachments (recursively, because Outlook PSTs get mailed as attachments too), identifying and attributing quoted text, deduplication of messages and documents, SNA, clustering, normalization, canonicalization, NER, synonym identification (aside from correctly identifying people referred to by nicknames, advanced implementations can also figure out when people are using a code-word to evade filters), etc.
- sherjilozair 10y ago
  You can ride your bike with no handlebars. ;)
garysieling 10y ago
One area I find interesting is classification. E.g. I'm building a search engine for lectures, so I built a utility that classifies search terms based on whether they are for a speaker or not, based on the text of the term on Wikipedia. The next stage will be to take articles posted to the various programming language subreddits, and build a classifier that classifies articles by language or topic. I did this previously for talks based on heuristics, but this will allow for a lot more categories: https://www.findlectures.com/?p=1&class1=Technology&category_l2_Technology=Programming%20Languages https://www.findlectures.com/?p=1&class1=Technology&category...
Kiro 10y ago
I don't know. I'm not even into deep learning but I still see things all the time I wish I could test deep learning on.
drc0 10y ago
deep learning is where databases where 20 years ago. you can easily imagine putting data in a db like server, and querying that data asking things about that model in respect to other things at your disposal in different times.

by nsxwolf 10y ago

malux85 10y ago

Everyones into Graphics programming, but what would I actually do with it? The uses are myriad, you can build Star Wars Movies, you can build computer games. You can do CAD and 3D modelling and printing, you can visualize fluid simulations But each is an entire field. The same with Deep Learning - You can do image classification for medical diagnosis. Self Driving Cars. Realtime Translation. OCR. You can model chemical reactions by doing latent space exploration. You can model gene-gene interactions. You can build a image recogniser to tell if your cat is on the couch. You can build an AI that plays GO, or any other system or game where the rewards are time-delayed and sparse. You can make it play Atari games. You can stitch satellite images together. You can reconstruct parts of photos that are missing. You can colour black and white images. You can de-noise wind sounds from microphones. You can search for comets. You can monitor deforestation. You can count cars in car parks. You can search vast ocean areas for survivors. You can build security drones that fly around at night and look for anomalies. You can turn a webpage of unstructured text into structured query-able forms (see named entity recognition). You can create visual art (google: neural style transfer) These are just a small drop of some of the cool things that are going on - and while it might be difficult for you to advance state of the art without a GPU cluster (just as it would be difficult to advance state of the art in Graphics programming) the hobbiest can certainly start in any of these fields, just pick one that inspires you.] EDIT: I run a Deep Learning startup - if you want some pointers or help getting started, or would like advice don't hesitate to email me - it's in my profile :)

gopher_protocol 10y ago
One of my partner's tasks as a legal assistant is to go through mountains of OCRed PDFs and classify them and extract pieces of data so that lawyers and paralegals can go through them more easily. Do you imagine deep learning would be an appropriate means of automating that, or is it overkill?
- malux85 10y ago
  No not overkill at all - I think the first step is to augment the human so the machine helps them, then it can totally replace all of the laborious stuff as time passes. Imagine something like: A deep network reduces a 20 page document to a summary of 4 or 5 sentences, you can click on these sentences to "expand" them out, eventually getting to the original text. Saving them from reading the whole document A separate classifier automatically classifies the document into one of say, 20 categories (or whatever is appropriate). A Deep Learning named entity recogniser extracts the Human names, Dates and times, Email addresses, Company Names, email addresses, Money amounts, and numbers from each document, then off to elasticsearch for indexing and easy searching. Then we can start to play with higher level legal concepts that (for example) set precedent, or search for certain logical fallacies. (the next step past machine learning is machine reasoning - and it's starting to be possible now)
- martingoodson 10y ago
  I have built deep learning systems for similar problem domains. I am happy to chat to you about your example. Email me if you get a chance.
- webmaven 10y ago
  Not overkill at all, but youy should know that there are a lot of competitors in the e-discovery software category, and they do a lot besides classification, starting from the ingestion of email archives, through extraction of attachments (recursively, because Outlook PSTs get mailed as attachments too), identifying and attributing quoted text, deduplication of messages and documents, SNA, clustering, normalization, canonicalization, NER, synonym identification (aside from correctly identifying people referred to by nicknames, advanced implementations can also figure out when people are using a code-word to evade filters), etc.
sherjilozair 10y ago
You can ride your bike with no handlebars. ;)

garysieling 10y ago

One area I find interesting is classification. E.g. I'm building a search engine for lectures, so I built a utility that classifies search terms based on whether they are for a speaker or not, based on the text of the term on Wikipedia. The next stage will be to take articles posted to the various programming language subreddits, and build a classifier that classifies articles by language or topic. I did this previously for talks based on heuristics, but this will allow for a lot more categories: https://www.findlectures.com/?p=1&class1=Technology&category_l2_Technology=Programming%20Languages https://www.findlectures.com/?p=1&class1=Technology&category...

Kiro 10y ago

I don't know. I'm not even into deep learning but I still see things all the time I wish I could test deep learning on.

drc0 10y ago

deep learning is where databases where 20 years ago. you can easily imagine putting data in a db like server, and querying that data asking things about that model in respect to other things at your disposal in different times.