Pinned Repositories
1on1-questions
Mega list of 1 on 1 meeting questions compiled from a variety to sources
60_Days_RL_Challenge
Learn Deep Reinforcement Learning in depth in 60 days
algorithmic-examples
Algorithmic Marketing Models
ARIMA-Tools
Tools in C++ to forecast time series using ARIMA models
ARM
Airline Revenue Management
assessing-and-improving-prediction-and-classification
Source code for 'Assessing and Improving Prediction and Classification' by Timothy Masters
atree
Just a simple Christmas tree, based on reddit story
auto-py-to-exe
Converts .py to .exe using a simple graphical interface
Baseball-feature-extraction
Tried to make assessment of which feature vectors (i.e. team-based statistics) are more highly correlated to winning. The ultimate actionable goal was to drive greater revenues by helping management leverage existing fixed assets by develop the right mix of variable assets (i.e. cost of players vs benefit of players) in order to create an organization with a greater likelihood of winning.
nmarwen's Repositories
nmarwen/1on1-questions
Mega list of 1 on 1 meeting questions compiled from a variety to sources
nmarwen/algorithmic-examples
Algorithmic Marketing Models
nmarwen/atree
Just a simple Christmas tree, based on reddit story
nmarwen/auto-py-to-exe
Converts .py to .exe using a simple graphical interface
nmarwen/blog
Source code and other material for my blog posts.
nmarwen/cookiecutter-data-science
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
nmarwen/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.
nmarwen/Financial-Models-Numerical-Methods
Collection of notebooks about quantitative finance, with interactive python code.
nmarwen/forecasting
Time Series Forecasting Best Practices & Examples
nmarwen/gists
Easily find my gists
nmarwen/HandsOn-Unsupervised-Learning-with-Python
HandsOn-Unsupervised-Learning-with-Python, Published by Packt
nmarwen/hello_tf_c_api
Neural Network TensorFlow C API.
nmarwen/homemade-machine-learning
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
nmarwen/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.
nmarwen/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.
nmarwen/machine_learning_refined
Notes, examples, and Python demos for the textbook "Machine Learning Refined" (Cambridge University Press).
nmarwen/MEDIUM_NoteBook
Repository containing notebooks of my posts on Medium
nmarwen/ML-DL-in-production
Repository, with some blogposts and code for deploying machine and deep learning-based models in production.
nmarwen/ml-projects
ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python
nmarwen/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 ;)
nmarwen/python_for_scientists
Python Open Courseware for Scientists and Engineers
nmarwen/PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
nmarwen/scipy_con_2019
Tutorial Sessions for SciPy Con 2019
nmarwen/scope_guard
Scope Guard & Defer C++
nmarwen/semver
Semantic Versioning C++
nmarwen/state_saver
State Saver C++
nmarwen/statrethinking_winter2019
Statistical Rethinking course at MPI-EVA from Dec 2018 through Feb 2019
nmarwen/Stock-Prediction-Models
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
nmarwen/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.
nmarwen/timeseries_demo
A short introduction to time series methods