petroimanol Goto Github PK

2016-ml-contest

Machine learning contest - October 2016 TLE

advertools

advertools - online marketing productivity and analysis tools

awesome-deep-learning-resources

Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier

complete-python-3-bootcamp

Course Files for Complete Python 3 Bootcamp Course on Udemy

cookbook

The Data Engineering Cookbook

cracking-the-coding-interview_solutions

Efficient solutions to "Cracking the Coding Interview" (6th Edition) problems

ct-gan

A GAN based framework for adding and removing medical evidence in 3D volumetric medical scans

data-analysis-over-10-years-of-hourly-energy-consumption-data-from-pjm-in-megawatts

Data Analysis Over 10 years of hourly energy consumption data from PJM in Megawatts python

deepschool.io

Deep Learning tutorials in jupyter notebooks.

deepwalk

Learn Node Embeddings from DeepWalk

dplp

A RST Parser with a trained model

ds-first-steps

english-words

:memo: A text file containing 479k English words for all your dictionary/word-based projects e.g: auto-completion / autosuggestion

evalne

Source code for EvalNE, a Python library for evaluating Network Embedding methods.

eventsim

Event data simulator. Generates a stream of pseudo-random events from a set of users, designed to simulate web traffic.

exploration

facies_classification

fake-news

Fake news detection

fakenews

CS224W Project

fakenewsdetector

fakenewsnet

This is a dataset for fake news detection research

gans

GANs in slanted land

geodatasets

Synthetic datasets for geoscience (geo)statistical modeling

hackerrank_solutions

317 efficient solutions to HackerRank problems

har-stacked-residual-bidir-lstms

Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.

hello-world

Just another repository

homemade-machine-learning

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

household-power-cosumption-lstm

human-activity-recognition-with-neural-network-using-gyroscopic-and-accelerometer-variables

The VALIDATION ACCURACY is BEST on KAGGLE. Artificial Neural Network with a validation accuracy of 97.98 % and a precision of 95% was achieved from the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this: The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.

introtodeeplearning

Lab Materials for MIT 6.S191: Introduction to Deep Learning