products User activity, business data, images, text, audio… Big Data technologies: Hadoop, Spark, Hive, Pig, Storm, Impala… Extract meaning from data and incorporate it into product: predicion, analytics, recommendations ! Technology ! Data ! Machine Learning
Learning is a „bigger and badder” approach to neural networks, which are known since 80’ y= g(x ⊗ W) Now we have much more computing power to train large (and deep) networks ⊞ Now we know better regularization and optimization methods ⊞ Now we have much more labeled data ⊞ Now we can also train models with unlabeled data ⊞
easier to infer that something is a face based on that it has two eyes and nose, than it has some black pixels in lower left corner, and white area somewhere in the middle
Numerical operations are very efficient, up to 100x faster than CPU ⊞ Single machine, no communication overhead ⊞ Significant memory contraints, we can’t train larger models ⊟
LEARNING RESEARCH TO IMPROVE PERSONALIZATION 10 BREAKTHROUGH TECHNOLOGIES 2013 GIGAOM GUIDE TO DEEP LEARNING: WHO’S DOING IT AND WHY IT MATTERS NYU „DEEP LEARNING” PROFESSOR LECUN WILL HEAD FACEBOOK’S NEW ARTIFICIAL INTELLIGENCE LAB Geoffrey Hinton Leading researcher in DL, his startup was acquired by Google Lookflow Deep Learning image startup, acquired by Yahoo DeepMind Deep Learning startup, acquired by Google for 400 mln USD Yan LeCun Leading researcher in DL, hired by Facebook to lead new AI lab.
DL, his startup was acquired by Google Lookflow Deep Learning image startup, acquired by Yahoo DeepMind Deep Learning startup, acquired by Google for 400 mln USD Yan LeCun Leading researcher in DL, hired by Facebook to lead new AI lab. Hype
It’s difficult. Sometimes it’s better to use simpler method. " # Nevertheless, it’s a very powerful technique, has attention of biggest IT companies and brings us closer to real artificial intelligence It requires substantial computing power and memory. Sometimes it’s not feasible to use deep learning models, especially if we have to train them regularly ! It’s kind of `black-box` Sometimes we can’t draw conclusions from learned features