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Introduction to Bayesian Modeling with PyMC3 - Dr. Juan Camilo Orduz
This post is devoted to give an introduction to Bayesian modeling using PyMC3, an open source probabilistic programming framework written in Python.
Python  Bayesian  PyMC3  tutorial  probability  statistics  math 
2 days ago by areich
Python Mode for Processing
You write Processing code. In Python.
Python  Processing  generativeart  art 
8 days ago by areich
How to Build a Blockchain in Python (Get Pre-built Runtime) | ActiveState
The system that Bitcoin relies upon — a growing list of records (i.e. blocks) that are linked to one another — is known as a blockchain. Bitcoin was the first successful application of this system, and shortly after its rise in popularity, other cryptocurrencies were founded on the same principles. This system, however, is not restricted to storing financial information. Rather, the type of data being stored is inconsequential to and independent of the blockchain network. 
Python  blockchain  bitcoin  tutorial 
5 weeks ago by areich
Building a Python C Extension Module – Real Python
Learn how to write Python interfaces in C. Find out how to invoke C functions from within Python and build Python C extension modules. You’ll learn how to parse arguments, return values, and raise custom exceptions using the Python API.
Python  C  extension  module  tutorial 
october 2019 by areich
How to Implement Bayesian Optimization from Scratch in Python
In this tutorial, you will discover Bayesian Optimization for directed search of complex optimization problems.
bayesian  optimization  python  tutorials 
october 2019 by areich
TensorFlow 2.0 + Keras Crash Course.ipynb - Colaboratory
This document serves as an introduction, crash course, and quick API reference for TensorFlow 2.0.
TensorFlow  Keras  tutorials  Python  ipynb  jupyternotebook  API 
october 2019 by areich
How to use Docker containers for new Data Scientists - Morioh
In this article we focus on one of the most popular new tools for data science and engineering - Docker.
Python  programming  Docker  datascience  tutorials 
october 2019 by areich
See the "from scratch" tagged articles
machinelearning  tutorials  blog  ML  Python  R  math  statistics 
october 2019 by areich
SuRF – Object RDF mapper — SuRF 1.2.0 documentation
SuRF is an Object - RDF Mapper based on the popular rdflib python library. It exposes the RDF triple sets as sets of resources and seamlessly integrates them into the Object Oriented paradigm of python in a similar manner as ActiveRDF does for ruby.
Python  RDF  RDFLib  object-oriented  semanticweb  SuRF 
september 2019 by areich
Welcome to Owlready2’s documentation! — Owlready2 0.20 documentation
Owlready2 is a package for ontology-oriented programming in Python. It can load OWL 2.0 ontologies as Python objects, modify them, save them, and perform reasoning via HermiT (included). Owlready2 allows a transparent access to OWL ontologies (contrary to usual Java-based API).
OWL  RDF  ontology  hermit  pellet  Python  reasoning  semanticweb 
september 2019 by areich
Keras for Beginners: Implementing a Convolutional Neural Network -
A beginner-friendly guide on using Keras to implement a simple Convolutional Neural Network (CNN) in Python.
deeplearning  neuralnetworks  CNN  convolutional  neural  network  Keras  tensorflow  Python  MNIST  academic 
august 2019 by areich
TensorFlow Probability  |  TensorFlow
TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. TFP includes:
TensorFlow  Probability  Python  DeepLearning  MachineLearning  TFP 
march 2019 by areich
Regression with Probabilistic Layers in TensorFlow Probability
Here, we demonstrate in more detail how to use TFP layers to manage the uncertainty inherent in regression predictions.
TensorFlow  probability  gaussianprocess  regression  tutorial  Python  machinelearning 
march 2019 by areich
How to Distribute a wxPython Application | The Mouse Vs. The Python
what you will learn here is how to turn your application into an executable
wxpython  tutorial  Python  GUI  distribution  executable 
march 2019 by areich
How to Build a Python GUI Application With wxPython – Real Python
In this article, you’ll learn how to build a graphical user interface with Python using the wxPython GUI toolkit.
wxpython  tutorial  Python  GUI 
march 2019 by areich
Machine Learning for Beginners: An Introduction to Neural Networks -
A simple explanation of how they work and how to implement one from scratch in Python.
machinelearning  neuralnetworks  tutorial  Python  blog 
march 2019 by areich
Generating Random Data in Python (Guide) – Real Python
Here, you’ll cover a handful of different options for generating random data in Python, and then build up to a comparison of each in terms of its level of security, versatility, purpose, and speed.
cryptography  Python  random  number  RNG  generator  tutorial 
january 2019 by areich
Jupyter Notebooks Advanced Tutorial
Following on from "Jupyter Notebook for Beginners: A Tutorial", this guide will take you on a journey from the truly vanilla to the downright dangerous. That's right! Jupyter's wacky world of out-of-order execution has the power to faze, and when it comes to running notebooks inside notebooks, things can get complicated fast.
Python  Jupyter  notebook  tutorial  advanced  blog 
january 2019 by areich
Alone Djangonaut – A tour on Python Packaging
If you're new to Python or a mature one and want to share your code with other developers or you have build a library to be used by end users and you're struggle with the packaging, then this tutorial/post/explanatory guide is (possibly) for you.
package  Python  library  tutorial 
november 2018 by areich
MCMC sampling for dummies
This blog post is an attempt at trying to explain the intuition behind MCMC sampling (specifically, the random-walk Metropolis algorithm). Critically, we'll be using code examples rather than formulas or math-speak. Eventually you'll need that but I personally think it's better to start with the an example and build the intuition before you move on to the math.
statistics  Bayesian  MCMC  tutorial  datascience  Python  sampling 
november 2018 by areich
Jupyter Notebook Viewer
This notebook was derived from the Caltech "Learning from Data" course, specifically Lecture 9 on the logistic regression model. It is a simple Python implementation of the logistic regression model using NumPy.
Python  Jupyter  notebook  logistic  regression  NumPy  tutorial 
november 2018 by areich
Foundations of Machine Learning
Bloomberg presents "Foundations of Machine Learning," a training course that was initially delivered internally to the company's software engineers as part of its "Machine Learning EDU" initiative. This course covers a wide variety of topics in machine learning and statistical modeling. The primary goal of the class is to help participants gain a deep understanding of the concepts, techniques and mathematical frameworks used by experts in machine learning. It is designed to make valuable machine learning skills more accessible to individuals with a strong math background, including software developers, experimental scientists, engineers and financial professionals.
machinelearning  course  videos  Bloomberg  Python  probability  statistics 
november 2018 by areich
Make a PEX from Python script | Peter Demin
Python is a great language for scripting. But there is a problem with distributing working executable. If script uses any non built-in dependency, it can’t be just copied to the target host and executed.

One possible solution is using PEX - Python EXecutable. It packs the script with dependencies inside a single binary.
Python  executable  PEX  tutorial  script 
november 2018 by areich
STA-663-2017 — STA-663-2017 1.0 documentation
The goal of STA 663 is to learn statistical programming - how to write code to solve statistical problems. In general, statistical problems have to do with the estimation of some characteristic derived from data - this can be a point estimate, an interval, or an entire function. Almost always, solving such statistical problems involves writing code to collect, organize, explore, analyze and present the data. For obvious reasons, we would like to write good code that is readable, correct and efficient, preferably without reinventing the wheel.
statistics  course  DukeUniversity  lectures  Python 
november 2018 by areich
What is Public Key Cryptography? - Twilio
This post will dive into modern cryptography, an overview of how it works, and its everyday use cases — including how Twilio uses public-key crypto in our Authy application and to secure our API.
Python  tutorial  cryptography  public-key  Twilio  Authy 
september 2018 by areich
PyQt5 tutorial: Create a Python GUI in 2018
This tutorial shows how you can use PyQt5 to build a desktop app with Python. It covers everything from the best way to set up PyQt in 2018, to compiling your app and distributing it to other people's computers. You can use Windows, Mac or Linux. The only prerequisite is that you have Python 3 (ideally 3.5) installed.
Python  GUI  tutorial  PyQt5  UI 
september 2018 by areich
Introduction to Bayesian modeling with PyMC3 | Dr. Juan Camilo Orduz
This post is devoted to give an introduction to Bayesian modeling using PyMC3, an open source probabilistic programming framework written in Python. Part of this material was presented in the Python Users Berlin (PUB) meet up.
Python  Bayesian  modeling  PyMC3  tutorial  MCMC  MarkovChains  Metropolis-Hastings  Coursera 
september 2018 by areich
Complete Guide to Topic Modeling - NLP-FOR-HACKERS
Topic modelling, in the context of Natural Language Processing, is described as a method of uncovering hidden structure in a collection of texts. Although that is indeed true it is also a pretty useless definition. Let’s define topic modeling in more practical terms.
topic  modeling  NLP  Python  TopicModeling  text-processing 
august 2018 by areich
clifford: Geometric Algebra for Python — Clifford 0.82 documentation
This module implements Geometric Algebras (a.k.a. Clifford algebras). Geometric Algebra (GA) is a universal algebra which subsumes complex algebra, quaternions, linear algebra and several other independent mathematical systems. Scalars, vectors, and higher-grade entities can be mixed freely and consistently in the form of mixed-grade multivectors.
geometricalgebra  Clifford  algebra  complexnumbers  quaternions  Python 
july 2018 by areich
Documentation for salabim — salabim 2.3.1 documentation
Salabim is a package for discrete event simulation in Python. It follows the methodology of process description as originally demonstrated in Simula and later in Prosim, Must and Tomas.
It is also quite similar to SimPy 2.

The package comprises discrete event simulation, queue handling, resources, statistical sampling and monitoring. On top of that real time animation is built in.

The package comes with a number of sample models.
Python  DES  discrete  event  simulation  salabim  documentation 
july 2018 by areich
Python Histogram Plotting: NumPy, Matplotlib, Pandas & Seaborn – Real Python
In this tutorial, you’ll be equipped to make production-quality, presentation-ready Python histogram plots with a range of choices and features.
Python  matplotlib  seaborn  histograms  plotting  numpy  pandas  tutorial 
july 2018 by areich
jason_delaat / PyMonad — Bitbucket
PyMonad is a small library implementing monads and related data abstractions -- functors, applicative functors, and monoids -- for use in implementing functional style programs. For those familiar with monads in Haskell, PyMonad aims to implement many of the features you're used to so you can use monads in python quickly and easily. For those who have never used monads but are interested, PyMonad is an easy way to learn about them in, perhaps, a slightly more forgiving environment without needing to learn Haskell.
Python  functional  monad  functors  monoids  PyMonad  module  library 
july 2018 by areich
A Simple Tutorial on How to document your Python Project using Sphinx and Rinohtype
In this tutorial, I’ll be using Sphinx and Rinohtype to produce an HTML and PDF documentation files respectively to a simple API project I wrote that manages a list of Teacher records (Github Project Link) .
Python  documentation  Sphinx  Rinohtype  tutorial 
june 2018 by areich
Building a Question-Answering System from Scratch— Part 1
This part will focus on introducing Facebook sentence embeddings and how it can be used in building QA systems.
Python  NLP  QA  QuestionAnswerSystems  sentence  embeddings  SQuAD 
june 2018 by areich
Developing Flask Apps in Docker Containers on macOS - Full Stack Python
Adding Docker to your Python and Flask development environment can be confusing when you are just getting started with containers. Let's quickly get Docker installed and configured for developing Flask web applications on your local system.
Python  Docker  Flask  MacOS  OSX  tutorials  containers  webapp  blog 
june 2018 by areich
NLP Architect by Intel® AI Lab — NLP Architect by Intel® AI Lab 0.1 documentation
NLP Architect is an open-source Python library for exploring the state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding. It is intended to be a platform for future research and collaboration.
NLP  Python  deeplearning  NLU  NER  Intel 
may 2018 by areich
Learn Python Programming Online – Real Python
At Real Python you can learn all things Python from the ground up. Everything from the absolute basics of Python, to web development and web scraping, to data visualization, and beyond
Python  tutorials  learning  howto 
may 2018 by areich
Part-of-Speech tagging tutorial with the Keras Deep Learning library
In this tutorial, you will see how you can use a simple Keras model to train and evaluate an artificial neural network for multi-class classification problems.
Python  NLTK  NLP  part-of-speech  POS  Keras  deeplearning  neuralnetwork  tutorial  blog 
april 2018 by areich
PyNaCl: Python binding to the libsodium library — PyNaCl 1.2.1 documentation
PyNaCl is a Python binding to libsodium, which is a fork of the Networking and Cryptography library.
PyNaCl  Python  cryptography  libsodium  documentation  hashing 
april 2018 by areich
Python Wheels
Wheels are the new standard of Python distribution and are intended to replace eggs.
Python  Wheels  distribution  eggs  packages 
april 2018 by areich
Python 3: An Intro to Encryption | The Mouse Vs. The Python
Python 3 doesn’t have very much in its standard library that deals with encryption. Instead, you get hashing libraries. We’ll take a brief look at those in the chapter, but the primary focus will be on the following 3rd party packages: PyCrypto and cryptography. We will learn how to encrypt and decrypt strings with both of these libraries.
cryptography  encryption  Python  tutorial  PyCrypto  hashing 
april 2018 by areich
Welcome to pyca/cryptography — Cryptography 2.3.dev1 documentation
cryptography includes both high level recipes and low level interfaces to common cryptographic algorithms such as symmetric ciphers, message digests, and key derivation functions.
cryptography  Python  API  pyca 
april 2018 by areich
Cryptography with Paul Kehrer – Episode 93 – Podcast.__init__
Sooner or later you will need to encrypt or hash some data. Thankfully we have the Cryptography library, along with the other projects maintained by the Python Cryptographic Authority, to make sure that your crypto is done right. In this episode Paul Kehrer talks about how the PyCA got started, the projects that they maintain, and how you can start using cryptography in your programs today.
Python  Cryptography  podcast  pyca  "Paul  Kehrer"  episode 
april 2018 by areich
Tutorial: What is WordNet? A Conceptual Introduction Using Python |
This tutorial is a gentle introduction to WordNet concepts, using TextBlob for the examples. To follow along with the examples, make sure you have the latest version of TextBlob.
Python  tutorial  WordNet  TextBlob  NLTK  NLP  blog 
february 2018 by areich
Handwritten digit recognizer on iOS with Keras and Core ML using the MNIST dataset
The goal of this tutorial is to show the full proceeding to create, train a Deep Learning model and to implement it in an iOS app. The use case here is the “Hello World” of Deep Learning, it is the digit recognition using a dataset of handwritten digits, the MNIST dataset. The model is created and trained by using the Keras framework and is then converted into a Core ML model in order to use it in an iOS app.
neuralnetworks  deeplearning  machinelearning  MNIST  helloworld  Keras  Python  TensorFlow  IOS  CoreMLTools  macOS  Xcode 
february 2018 by areich
Build a Neural Network with Python
Neural networks can be intimidating, especially for people new to machine learning. However, this tutorial will break down how exactly a neural network works and you will have a working flexible neural network by the end. Let’s get started!
neuralnetworks  deeplearning  machinelearning  Python  tutorial 
february 2018 by areich
Phonetic Similarity of Words: A Vectorized Approach in Python
Seen as a proposal, this article demonstrates how to combine different phonetic algorithms in a vectorized approach, and to use their peculiarities in order to achieve a better comparison result than using the single algorithms separately. To implement this, the Python-based library named AdvaS Advanced Search on SourceForge comes into play. AdvaS already includes a method in order to calculate several phonetic codes for a word in a single step.
Python  soundex  phonetic  similarity  vector  words  NLP 
february 2018 by areich
aGrUM/pyAgrum – aGrUM/pyAgrum
pyAgrum is a Python wrapper for the C++ aGrUM library. It provides a high-level interface to the part of aGrUM allowing to create, handle and make computations into Bayesian Networks.

The module mainly is a application of the SWIG interface generator. Custom-written code is added to simplify and extend the aGrUM API.
Python  C++  aGrUM  pyAgrum  BayesianNetworks  GraphicalModels  library  InfluenceDiagrams  DecisionTrees 
january 2018 by areich
Understanding and building Generative Adversarial Networks(GANs)- Deep Learning with PyTorch.
We’ll be building a Generative Adversarial Network that will be able to generate images of birds that never actually existed in the real world.
deeplearning  neuralnetworks  GAN  generative  adversarial  birds  Python 
january 2018 by areich
Time Series Analysis in Python: An Introduction – Towards Data Science
This post will walk through an introductory example of creating an additive model for financial time-series data using Python and the Prophet forecasting package developed by Facebook. Along the way, we will cover some data manipulation using pandas, accessing financial data using the Quandl library and, and plotting with matplotlib. I have included code where it is instructive, and I encourage anyone to check out the Jupyter Notebook on GitHub for the full analysis. This introduction will show you all the steps needed to start modeling time-series on your own!
Python  TimeSeries  Prophet  financial  forecasting  Facebook  AdditiveModel  Quandl 
january 2018 by areich
Cryptocurrency Analysis with Python - MACD | Roman Orac blog
Cryptocurrencies are becoming mainstream so I’ve decided to spend the weekend learning about it. I’ve hacked together the code to download daily Bitcoin prices and apply a simple trading strategy to it.

Note that there already exists tools for performing this kind of analysis, eg. tradeview, but this way enables more in-depth analysis.
bitcoin  cryptocurrency  Python  trading  analysis  blog  MACD  moving_average 
december 2017 by areich
A friendly Introduction to Backpropagation in Python | Sushant Choudhary
My aim here is to test my understanding of Karpathy’s great blog post “Hacker’s guide to Neural Networks” as well as of Python, to get a hang of which I recently perused through Derek Banas’ awesome commented code expositions. As someone steeped in R and classical statistical learning methods for structured data, I’m very new to both Python as well as Neural nets, so it is best not to fall into the easy delusions of competence that stem from being able to follow things while reading about them. Therefore, code.
tutorial  machinelearning  blog  neuralnetwork  Python 
december 2017 by areich
TensorFlow Neural Network Tutorial
TensorFlow applications can be written in a few languages: Python, Go, Java and C. This post is concerned about its Python version, and looks at the library's installation, basic low-level components, and building a feed-forward neural network from scratch to perform learning on a real dataset.
TensorFlow  neuralnetworks  tutorial  blog  Python 
november 2017 by areich
Inference Over RDF Containers
In this notebook I explore the use of inference to simplify queries and show a set of example queries that illustrate patterns for making queries against RDF Containers.
RDFS  containers  inference  semanticweb  rdflib  gastrodon  Python  iPython-Notebook  queries 
november 2017 by areich
In this notebook, I begin the process of analyzing the schema of the DBpedia Ontology. This is a local notebook in which I load data from the filesystem into an in-memory graph, thus it is part of the unit tests for gastrodon. This is feasible because the schema is much smaller than DBpedia as a whole.
semanticweb  DBpedia  ontology  gastrodon  schema  graph  wikipedia  RDFS  OWL  iPython-Notebook  rdflib  Python 
november 2017 by areich
Christian Borgelt's Web Pages
PyFIM is an extension module that makes several frequent item set mining implementations available as functions in Python 2.7.x & 3.5.x. Currently apriori, eclat, fpgrowth, sam, relim, carpenter, ista, accretion and apriacc are available as functions, although the interfaces do not offer all of the options of the command line program. (Note that lcm is available as an algorithm mode of eclat.) There is also a "generic" function fim, which is essentially the same function as fpgrowth, only with a simplified interface (fewer options). Finally, there is a function arules for generating association rules (simplified interface compared to apriori, eclat and fpgrowth, which can also be used to generate association rules.
Python  PyFIM  FrequentItemsetMining  FIM  apriori  algorithms 
november 2017 by areich
Build Your Own Blockchain Part 1 — Creating, Storing, Syncing, Displaying, Mining, and Proving Work | Big-Ish Data
In this post, I’ll show the way I want to store the blockchain data and generate an initial block, how a node can sync up with the local blockchain data, how to display the blockchain (which will be used in the future to sync with other nodes), and then how to go through and mine and create valid new blocks. For this first post, there are no other nodes. There are no wallets, no peers, no important data. Information on those will come later.
tutorial  blockchain  bitcoin  Python 
october 2017 by areich
Implementing Fisher’s LDA from scratch in Python · Hardik Goel
Fisher’s Linear Discriminant Analysis (LDA) is a dimension reduction technique that can be used for classification as well. In this blog post, we will learn more about Fisher’s LDA and implement it from scratch in Python.
Python  LinearDiscriminantAnalysis  LDA  PrincipalComponentAnalysis  PCA  dimensionreduction  classification  FIsher 
october 2017 by areich
Machine Learning and Artificial Intelligence
Personal notes on statistics, data science, and programming. Everything from regression assumptions to distributed computing.
machinelearning  datascience  statistics  examples  blog  Python 
october 2017 by areich
Using categorical data in machine learning with python: from dummy variables to Deep category…
In this post I will share some basic strategies of using categorical data that worked for us (at YellowRoad) in recent projects, while on part 2, I will share some more advanced methods. I will discuss on why they work and why different methods are better for different algorithms in different scenarios, while sharing my code implementation in Python.
Python  categorical  data  labelencoding  one-hot  encoding  dummyvariables  feature  hashing 
october 2017 by areich
The Python Dictionary—The Sharat's
The Python Dictionary is a key–value style data structure that is tightly integrated with the language syntax and semantics. Understanding them well can help us use them better and investigate subtle problems more efficiently.

This is my attempt to document this topic in more depth. Though I included a small section about the syntax and basic usage of dictionaries, it’ll be helpful if you have some beginner–intermediate level experience with Python.

This article is written for Python 3.6 installed via Anaconda on Xubuntu.
Python  dictionary  tutorial  blog  key-value 
october 2017 by areich
Seven Things You Might Not Know about Numba | Parallel Forall
The productivity and interactivity of Python combined with the high performance of GPUs is a killer combination for many problems in science and engineering. There are several approaches to accelerating Python with GPUs, but the one I am most familiar with is Numba, a just-in-time compiler for Python functions. Numba runs inside the standard Python interpreter, so you can write CUDA kernels directly in Python syntax and execute them on the GPU.
Python  Numba  compilers  GPU  CPU  CUDA  Jupyter  arrays 
october 2017 by areich
How to Generate FiveThirtyEight Graphs in Python
Using Python’s matplotlib and pandas, we’ll see that it’s rather easy to replicate the core parts of any FiveThirtyEight (FTE) visualization.
Python  visualization  FiveThirtyEight  FTE  matplotlib  pandas  graphs  tutorial  blog 
october 2017 by areich
A guide to logging in Python |
This article looks at Python's logging module, its design, and ways to adapt it for more complex use cases.
Python  logging  tutorial  blog 
october 2017 by areich
PyNaCl: Python binding to the libsodium library — PyNaCl 1.2.0.dev1 documentation
PyNaCl is a Python binding to libsodium, which is a fork of the Networking and Cryptography library. These libraries have a stated goal of improving usability, security and speed. It supports Python 2.7 and 3.3+ as well as PyPy 2.6+.
encryption  cryptography  Python  libsodium  NaCl  PyNaCl 
october 2017 by areich
NLP Tutorial Using Python NLTK (Simple Examples) - Like Geeks
In this post, we will talk about natural language processing (NLP) using Python. This NLP tutorial will use Python NLTK library. NLTK is a popular Python library which is used for NLP.
NLP  NLTK  Python  tutorial  blog 
october 2017 by areich
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