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Mohamed Abuella's Projects

cymysql icon cymysql

CyMySQL: Python MySQL Client powered by Cython

d2l-en icon d2l-en

Dive into Deep Learning, Berkeley STAT 157 (Spring 2019) textbook. With code, math, and discussions.

d3 icon d3

Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:

darkflow icon darkflow

Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

darts icon darts

A python library for easy manipulation and forecasting of time series.

data-driven-ship-fuel-efficiency-modeling icon data-driven-ship-fuel-efficiency-modeling

This projects adopts machine learning models to quantify the daily/hourly bunker fuel consumption of a ship in different sailing speed, displacement/draft, trim, weather, and sea conditions. The industry data utilized include voyage report data, sensor data, AIS data, and meteorological data. Apart from Python code, here, we also share 130 trained machine learning models that enable you to estimate the daily/hourly fuel consumption of eight 8100-TEU to 14,000-TEU containerships. To download our trained machine learning mdoels and use them for estimate/forecost ship fuel consumption rate (ton/day, ton/hour), read "Instructions on How to Use Trained Machine Learning Models". For our Python code, read "Readme - Organization of Python Code Files in This Project".

data-science icon data-science

Collection of useful data science topics along with code and articles

data-science-45min-intros icon data-science-45min-intros

Ipython notebook presentations for getting starting with basic programming, statistics and machine learning techniques

datacamp icon datacamp

R package to create interactive courses for www.datacamp.com

deep-forecast icon deep-forecast

The code of the paper 'Deep Forecast : Deep Learning-based Spatio-Temporal Forecasting", ICML Time Series Workshop 2017.

deep-learning-1 icon deep-learning-1

Projects include the application of transfer learning to build a convolutional neural network (CNN) that identifies the artist of a painting, the building of predictive models for Bitcoin price data using Long Short-Term Memory recurrent neural networks (LSTMs) and a tutorial explaining how to build two types of neural network using as input the MNIST dataset, namely, a CNN using Keras and a fully-connected network using TensorFlow.

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