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Data Science Weekly Newsletter
April 11, 2019
Unsupervised learning: The curious pupil
Our new blog post overviews unsupervised learning, a paradigm for creating artificial intelligence that learns about data without a particular task in mind. Read more about how we might teach computers to learn for the sake of learning...
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Google announces AI Platform, an end-to-end service
The highlight of today’s announcements is the beta launch of the company’s AI Platform. The idea here is to offer developers and data scientists an end-to-end service for building, testing and deploying their own models. To do this, the service brings together a variety of existing and new products that allow you to build a full data pipeline to pull in data, label it (with the help of a new built-in labeling service) and then either use existing classification, object recognition or entity extraction models, or use existing tools like AutoML or the Cloud Machine Learning engine to train and deploy custom models....
Mapping for humanitarian aid and development with weakly - and semi-supervised learning
When disaster or disease strikes, relief agencies respond more effectively when they have detailed mapping tools to know exactly where to deliver assistance. But extremely reliable and precise maps often are not available. So, our team, composed of artificial intelligence researchers and data scientists in Facebook's Boston office, used our computer vision expertise to create and share population density maps that are more accurate and higher resolution than any of their predecessors...
Unsupervised Recurrent Neural Network Grammars
In this work, we experiment with unsupervised learning of RNNGs. Since directly marginalizing over the space of latent trees is intractable, we instead apply amortized variational inference. To maximize the evidence lower bound, we develop an inference network parameterized as a neural CRF constituency parser....
Implementing Probabilistic Matrix Factorization in PyTorch
I found myself wanting to learn more about models for recommendation systems. After a bit of digging, I found what appears to be one of the better options for collaborative filtering called Probabilistic Matrix Factorization (PMF). What really excited me about this particular model is that it is a pretty straightforward Bayesian model. Also, implementing it with PyTorch would be quite fun. I'll outline the idea of PMF in this post...
Predictive Analytics World (PAW) brings together five co-located industry-specific events in Las Vegas: PAW Business, PAW Financial, PAW Industry 4.0, PAW Healthcare and Deep Learning World, gathering the top practitioners and the leading experts in data science and machine learning. By design, this mega-conference is where to meet the who's who and keep up on the latest techniques, making it the leading machine learning event. On stage: Google, Apple, Uber, Facebook, LinkedIn, Twitter and more...
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Reproducible Research with R and R Studio
"a very practical book that teaches good practice in organizing reproducible data analysis and comes with a series of examples..."...
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