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Data Science of Visiting Famous Movie Locations in San Francisco
Using the Google Places API and IMDb API, we selected movie locations in The Golden City which every movie fan should visit while they are in town, and optimize sightseeing by solving the travelling salesman problem.
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Theoretical Data Discovery: Using Physics to Understand Data Science
Data science may be a relatively recent buzzword, but the collection of tools and techniques to which it refers come from a broad range of disciplines. Physics has a wealth of concepts to learn from, as evidenced in this piece.
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Build vs Buy – Analytics Dashboards
Read this post on choosing between available analytics dashboard options, and designing your own. Get an informed opinion.
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Data Science Statistics 101
Statistics can often be the most intimidating aspect of data science for aspiring data scientists to learn. Gain some personal perspective from someone who has traveled the path.
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Would You Survive the Titanic? A Guide to Machine Learning in Python Part 2
This is part 2 of a 3 part introductory series on machine learning in Python, using the Titanic dataset.
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Would You Survive the Titanic? A Guide to Machine Learning in Python Part 1
Check out the first of a 3 part introductory series on machine learning in Python, fueled by the Titanic dataset. This is a great place to start for a machine learning newcomer.
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Building a Data Science Portfolio: Machine Learning Project Part 1
Dataquest's founder has put together a fantastic resource on building a data science portfolio. This first of three parts lays the groundwork, with subsequent posts over the following 2 days. Very comprehensive!
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Multi-Task Learning in Tensorflow: Part 1
A discussion and step-by-step tutorial on how to use Tensorflow graphs for multi-task learning.
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In Deep Learning, Architecture Engineering is the New Feature Engineering
A discussion of architecture engineering in deep neural networks, and its relationship with feature engineering.
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MNIST Generative Adversarial Model in Keras
This post discusses and demonstrates the implementation of a generative adversarial network in Keras, using the MNIST dataset.
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