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Capstone: Retrieving, Processing, and Visualizing Data with Python Coursera

@machinelearnbot

In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book "Python for Everybody" will serve as the backbone for the capstone. This course covers Python 3. Approx.


Picasso: A free open-source visualizer for CNNs – merantix – Medium

@machinelearnbot

While it's easier than ever to define and train deep neural networks (DNNs), understanding the learning process remains somewhat opaque. Monitoring the loss or classification error during training won't always prevent your model from learning the wrong thing or learning a proxy for your intended classification task. Regardless of the veracity of this tale, the point is familiar to machine learning researchers: training metrics don't always tell the whole story. And the stakes are higher than ever before: for rising applications of deep learning like autonomous vehicles, these kinds of training errors can be deadly [2]. Fortunately, standard visualizations like partial occlusion [3] and saliency maps [4] provide a sanity check on the learning process.


50 great data visualizations

@machinelearnbot

I think many of these visualizations are just pure art, but delivering no insight. So we selected a few of the most powerful ones for you. Click here to check the 50 visualizations. Check out our previous article on great visualizations. Or do a DSC search to find all our articles about visualization.


Instagrammers in the Louvre, tracked by geo data [OC] • /r/dataisbeautiful

#artificialintelligence

A place for visual representations of data: Graphs, charts, maps, etc. DataIsBeautiful is for visualizations that effectively convey information. Aesthetics are an important part of information visualization, but pretty pictures are not the aim of this subreddit. Check the best user-made visualizations of 2013 and 2014 (Jan-July Aug-Dec). Directly link to the original source article of the visualization (not an image file) or tag the post as [OC] if you made the visualization. Only [OC] posts may link to image files.


The 5 Common Mistakes That Lead to Bad Data Visualization

@machinelearnbot

Data visualization is a great way to represent huge amounts of data in a simple and intuitive fashion. All data visualizations have the same goal: help viewers easily grasp information to make quick inferences or decisions. However, it is important that visualizations are not overdone and hit the sweet spot where they are catchy, informative, and easy to navigate. This requires a bit of learning. Putting up a good data visualization is not just a matter of throwing together some data in colorful charts.