Training
When thinking about enhancing R&D processes, Newton’s second law of motion provides the perfect framework. Classical mechanics teaches us that putting a body into motion requires applying force. The resulting acceleration will be the sum of the forces applied to the body, divided by the body’s mass: a = F/m. So, if we want to…
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What can we learn about machine learning from ancient astronomy? When thinking about Machine Learning it is easy to be model-centric and get caught up in the details of getting a new model up and running: preparing a dataset for machine learning, partitioning the training and test data, engineering features, selecting features, finding an appropriate…
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Dear Students and Friends of Enthought, I am pleased to announce Enthought Academy—the culmination of over twenty years of teaching Scientific Python. Since our founding in 2001, Enthought has worked in the Scientific Python ecosystem, consulting in both the public and private sectors, solving hard science problems. As the creators of the SciPy package, cofounders…
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In the blog post Configuring a Neural Network Output Layer we highlighted how to correctly set up an output layer for deep learning models. Here, we discuss how to make sense of what a neural network actually returns from the output layers. If you are like me, you may have been surprised when you first…
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Introduction When we teach our foundational Python class, one of the things we do is make sure that our students know how to explore Python from the command line. This has several advantages. First, it reduces context switching – to figure out new stuff, students don’t constantly have to toggle between writing Python code and…
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Introduction When I first started my machine learning journey, K-means clustering was one of the first algorithms I was introduced to – and it is still one of my favorites to this day. I was amazed at how elegant yet comprehensible the procedure was. There is something oddly satisfying about watching the cluster assignments and…
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Introduction At Enthought we teach a lot of scientists and engineers about using Python and the ecosystem of scientific Python packages for processing, analyzing, and visualizing data. Most of what we teach involves nice, clean data sets–collections of data that have been carefully collected, scrubbed, and prepared for analysis. While we also mention in passing…
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Introduction If you have used TensorFlow before, you know how easy it is to create a simple neural network model using the Keras API. Just create an instance of the Sequential model class, add the number of desired layers and accompanying layer nodes, define the activation functions to be used by each layer, and compile…
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One of the best features of Python is that it is platform independent. You can write code on Linux, Windows, and MacOS and it works on all three platforms with no problems…mostly. Admittedly there are some issues. Most of these are from known operating system differences when accessing system subprocesses or dealing with various local…
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Introduction So, you have data and want to get started with machine learning. You’ve heard that machine learning will help you make sense of that data; that it will help you find the hidden gold within. Before you start sifting through your metaphorical gold mine, you realize you still have some unanswered questions: How do I…
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