Write Unbreakable Python – Software, Fitness, and Gaming – Jesse Warden
In this article, I’ll show you how to write Python with no runtime exceptions. This’ll get you partway to writing code that never breaks and mostly does what it’s supposed to do. We’ll do this by learning how to apply functional programming to Python. We’ll cover: ensure functions always work by learning Pure Functionsavoid runtime errors by return Maybesavoid runtime errors in deeply nested data
AWS Lambda NodeJS 8 vs. Python 3.7 vs Golang v1.x
Full disclosure, I primarily work with NodeJS on a daily basis but fully recognize that in order to solve a problem you must use the best tool for the job. I had to consume an AWS Kinesis stream with Lambda but had some questions surrounding what language I should use for the Lambda function. So I set out to test mainly the bootstrapping process and warm/cold starting of the underlying docker inst
Yet another “Why my company chose Elixir” story
Alchemist Sendivogius By Jan Matejko — pl.pinterest.com, Public Domain, https://commons.wikimedia.org/w/index.php?curid=67711Everybody knows Python, right?!When we started Pylon and began building our first conversational platform, we chose the tools that allowed everyone to contribute on day one: Python, Django, PostgreSQL, and Redis. As with any foray into uncharted territory, the first attempt
Mixins in Python and Ruby Compared – Andrew Brookins
The venerable “mixin” is a technique I learned as a Python developer. Now, after writing Ruby code for the past year, I’m excited to compare how these two languages approach mixins, including similarities, differences, and traps. There will be code! Table of Contents What is a Mixin? Mixins in Python Adding Instance Methods Adding Class Methods Trap: The Order of Superclasses Matters! Adding Insta
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Comparison of programming languages (list comprehension) - Wikipedia
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Hello! I'm a data scientist at Zymergen. Zymergen creates novel materials using microorganisms. We do this using a combination of rational genetic edits and high-throughput testing to determine which edits worked and which didn't. As a data scientist, I build models to separate signal from noise and find the successful needle in the haystack of all our attempted edits. Astronomy Before becoming a
Keras バックエンド速度比較 CNTK vs TensorFlow - Qiita
from __future__ import print_function import time start_time = time.time() import keras from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout from keras.optimizers import RMSprop batch_size = 128 num_classes = 10 epochs = 20 # the data, shuffled and split between train and test sets (x_train, y_train), (x_test, y_test) = mnist.load_data() x_tr
Comparison of Calibration of Classifiers
Note Go to the end to download the full example code. or to run this example in your browser via JupyterLite or Binder Comparison of Calibration of Classifiers# Well calibrated classifiers are probabilistic classifiers for which the output of predict_proba can be directly interpreted as a confidence level. For instance, a well calibrated (binary) classifier should classify the samples such that fo
Comparison of LDA and PCA 2D projection of Iris dataset
Note Go to the end to download the full example code. or to run this example in your browser via JupyterLite or Binder Comparison of LDA and PCA 2D projection of Iris dataset# The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width, petal length and petal width. Principal Component Analysis (PCA) applied to this data ident
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