GoCD now integrates natively with Kubernetes! GoCD's pipeline capability along with Kubernetes' highly programmable platform provide you the premiere Continuous Delivery tool on modern infrastructure.
The Google Ngram viewer is a fun/useful tool that uses Google’s vast trove of data scanned from books to plot word usage over time.
SQL was a go-to tool when you needed to get a quick-and-dirty look at some data, and draw preliminary conclusions that might, eventually, lead to a report or an application being written. This is called exploratory analysis.
These days, data comes in many shapes and forms, and it’s not synonymous with “relational database” anymore. You may end up with CSV files, plain text, Parquet, HDF5, and who knows what else. This is where Pandas library shines.
Topic Modeling is a technique to extract the hidden topics from large volumes of text. Latent Dirichlet Allocation(LDA) is a popular algorithm for topic modeling with excellent implementations in the Python’s Gensim package. The challenge, however, is how to extract good quality of topics that are clear, segregated and meaningful. Tthis depends heavily on the quality of text preprocessing and the strategy of finding the optimal number of topics. This tutorial attempts to tackle both of these problems.
Three years after my definitive guide on Python classic, static, class and abstract methods, it seems to be time for a new one. Here, I would like to dissect and discuss Python exceptions.
4 Ways to Improve Your DevOps Testing.
Predigame is an instructional platform that teaches the basics of coding through the creation of arcade games.
Python and Erlang are two programming languages, which I cannot get enough of. Having said that my first crush is Pascal and first love is C although that story is for some other time. It is interesting how they compliment each other in so many ways making this combination amazing for a developer.
I will be using the excellent CausalInference package to give an overview of how we can use the potential outcomes framework to try and make causal inferences about situations where we only have observational data.
When I started with Python, there were not yet NumPy or SymPy. We used to do research in MatLab, rapid prototyping in Delphi and, believe me or not, symbolic computation in Prolog. Python was something like a nicer Perl, not really popular with the researchers. But it was fun to write things in.
Write PyTorch code at the level of individual examples, then run it efficiently on minibatches.
Tools for converting Python code to AWS Step Function json
Presentation: Debugging across pipes and sockets with strace
Susnote is a notebook which support markdown, UML and Chart.
Automated Chromebook loaner management
Software-defined radio application written in Python
Simple cryptocurrency mining honeypot
Library for executing customizable script-languages in python
Lets you transfer files and directories from your computer to your mobile device by scanning a QR code right from the terminal.
Generic file search & replace tool, written in Python 3
Undo/Redo support for Maya Python API 2.0
a geographical visualization
Plot statistical distributions in your terminal
Fetch comments from the given video and determine sentiment towards the video is positive or negative