nmarwen

nmarwen

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nmarwen's repositories

1on1-questions

Mega list of 1 on 1 meeting questions compiled from a variety to sources

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algorithmic-examples

Algorithmic Marketing Models

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atree

Just a simple Christmas tree, based on reddit story

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auto-py-to-exe

Converts .py to .exe using a simple graphical interface

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blog

Source code and other material for my blog posts.

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cookiecutter-data-science

A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

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dowhy

DoWhy is a Python library that makes it easy to estimate causal effects. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

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Financial-Models-Numerical-Methods

Collection of notebooks about quantitative finance, with interactive python code.

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forecasting

Time Series Forecasting Best Practices & Examples

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gists

Easily find my gists

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HandsOn-Unsupervised-Learning-with-Python

HandsOn-Unsupervised-Learning-with-Python, Published by Packt

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hello_tf_c_api

Neural Network TensorFlow C API.

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homemade-machine-learning

🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

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Introduction-to-Time-Series-forecasting-Python

Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.

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Kalman-and-Bayesian-Filters-in-Python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

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machine_learning_refined

Notes, examples, and Python demos for the textbook "Machine Learning Refined" (Cambridge University Press).

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MEDIUM_NoteBook

Repository containing notebooks of my posts on Medium

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ML-DL-in-production

Repository, with some blogposts and code for deploying machine and deep learning-based models in production.

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ml-projects

ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python

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Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

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python_for_scientists

Python Open Courseware for Scientists and Engineers

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PythonDataScienceHandbook

Python Data Science Handbook: full text in Jupyter Notebooks

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scipy_con_2019

Tutorial Sessions for SciPy Con 2019

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scope_guard

Scope Guard & Defer C++

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semver

Semantic Versioning C++

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state_saver

State Saver C++

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statrethinking_winter2019

Statistical Rethinking course at MPI-EVA from Dec 2018 through Feb 2019

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Stock-Prediction-Models

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

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stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

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timeseries_demo

A short introduction to time series methods

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