The Sound of Lac Simon

The Sound of Lac Simon

The constant gentle lapping of waves on sand, interrupted by a becoming of a high pitched whine of insects, like bandsaw on wood, joining the rumble of the motor boat that masks the yelps of the collapsing water skier, on top of the skipping sound as speed boat whines and bounces across the surface of the lake, the gentle lapping becomes a clapping, intermingled with the buzz of seaplanes whose flight path mimics that of a small bird, whilst tilting like a whale getting a better view of a passing boat. The crescendo falls and the lapping re-asserts itself for a time, only for it to arise anew. 

Heidegger: Enframing determines a new type of causality

Heidegger: Enframing determines a new type of causality

Technology creates a fundamental anticipation which directs the activity of production itself, so that the actual thing produced, is merely a byproduct that is consistent with, but that which fails to fully discharge, a potential that is brought-forth in this act of creation. This truth of technology, exceeds truth as correspondence, and is expressed best in poetry.

This primordial Greek understanding of what is essential to technology, is perverted, initially by Plato whom distinguished the word play of poetry from that of science. We doubled down on this error through the use of modern technology, that penetrates nature, for ends which are not those of what is penetrated.

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Heidegger on causality: On the return of the Greek

Heidegger on causality: On the return of the Greek

Heidegger challenge to us is to think efficient causality as being derived from a more primary sense of cause considered as final cause,min which the effects of cause are considered internal to cause itself. A proper understanding of causality will go on to give us access to the truth of technology.

He does this through an almost Hegelian argument, but first let us examine his example of the silver chalice:

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Input/Output files for the uninitiated

Input/Output files for the uninitiated

In this series of posts we follow the Kwaggle Python Tutorial for constructing a model to predict deaths on the Titanic. Whereas the Kwaggle tutorial is a crash course on using Python to make and submit a basic model to the competition, my purpose is to go through the tutorial methodically. I will peek behind the curtain to shed light on how Python implements the model.

We followed the tutorial in the first post of the series as it read a training dataset and in  the second post we built a model to predict survival on the Titanic based on gender. In this third post we will follow the tutorial as it looks to apply the model on a test dataset which will provide an output for submission.

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From lists to numpy arrays for the uninitiated

From lists to numpy arrays for the uninitiated

In this series of posts we follow the Kwaggle Python Tutorial for constructing a model to predict deaths on the Titanic. Whereas the Kwaggle tutorial is a crash course on using Python to make and submit a basic model to the competition, my purpose is to go through the tutorial methodically. I will peek behind the curtain to shed light on how Python implements the model.

We pick up the Kwaggle tutorial from where we left off last time, and use it as an excuse to explore lists, numpy arrays and changing datatypes. We demonstrate how it is possible to mimic numpy array functions using lists, and how it is still probably better just to use numpy arrays. We follow the Kwaggle tutorial in order to select gender as the independent variable to predict survival rates on the Titanic.

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File objects, iterators, and list comprehensions for the uninitiated

File objects, iterators, and list comprehensions for the uninitiated

In this series of posts we follow the Kwaggle Python Tutorial for constructing a model to predict deaths on the Titanic. Whereas the Kwaggle tutorial is a crash course on using Python to make and submit a basic model to the competition, my purpose is to go through the tutorial methodically. I will peek behind the curtain to shed light on how Python implements the model.

This post follows the tutorial to the extent that it imports data from a CSV file into Python.  In doing so it expounds the concepts of file objects, iterables, iterators, and for loops.

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