mortenhaga

mortenhaga

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RRL_Matlab

Recurrent Reinforcement Learning Algorithm Matlab Implementation

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openai_lab

An experimentation framework for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras.

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evolution-strategies-starter

Code for the paper "Evolution Strategies as a Scalable Alternative to Reinforcement Learning"

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awesome-deep-learning-papers

The most cited deep learning papers

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non-overwhelming-machine-learning

A (non overwhelming) list of Machine Learning resources for beginners

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deep-learning-book

Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

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awesome-functional-python

A curated list of awesome things related to functional programming in Python.

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Deep-Learning-Papers-Reading-Roadmap

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

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numpy_exercises

Numpy exercises.

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udacity-machine-learning-nanodegree

project solutions to Udacity's Machine Learning Nanodegree

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tsfresh

Automatic extraction of relevant features from time series:

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python-machine-learning-book

The "Python Machine Learning (1st edition)" book code repository and info resource

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PythonTrainingExercises

Code to exercise your Python knowledge.

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arctic

High performance datastore for time series and tick data

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Creating-Customer-Segments

Udacity Machine Learning Nanodegree Project 3. Reduced dimensionality from 6 to 2 using PCA. Applied clustering using Gaussian Mixtures. Interpreted results and proposed recommendations for improved business processes.

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MLND-P3-Creating-Customer-Segments

Udacity Machine Learning Engineer Nanodegree Project 3

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boston-housing

Project 1 of Udacity's Machine Learning Engineer course

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Titanic-Survival-Exploraton

This repository contains project file for Project 0 - Titanic Survival Exploration as part of Udacity's Machine Learning Nanodegree.

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Building-a-Student-Intervention-System

Udacity Machine Learning Nanodegree Project 2. Investigated the factors that affect a student's performance in high school. Trained and tested several supervised machine learning models on a given dataset to predict how likely a student is to pass. Selected the best model based on relative accuracy and efficiency.

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