Google Street View House Number(SVHN) Dataset, and classifying them through CNN
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Updated
Mar 4, 2018 - Jupyter Notebook
Google Street View House Number(SVHN) Dataset, and classifying them through CNN
Demo on the capability of Yandex CatBoost gradient boosting classifier on a fictitious IBM HR dataset obtained from Kaggle. Data exploration, cleaning, preprocessing and model tuning are performed on the dataset
A command-line utility program for automating the trivial, frequently occurring data preparation tasks: missing value interpolation, outlier removal, and encoding categorical variables.
Analysis and preprocessing of the kdd cup 99 dataset using python and scikit-learn
Deeplearning4J框架搭建的第一个问答小AI
Adaptive Reinforcement Learning of curious AI basketball agents
Keras 응용(CNN, RNN, GAN, DNN, ETC...) 사용법 예시
This repository contains Sentiment Classification, Word Level Text Generation, Character Level Text Generation and other important codes/notes on NLP. Python and Keras are used for implementation.
Movie Recommendation System
Determining the housing prices of California properties for new sellers and also for buyers to estimate the profitability of the deal.
Customer churn analysis for a telecommunication company
Semantic Segmentation Using U-Net Architecture
Machine-learning models to predict whether customers respond to a marketing campaign
Implementation of Character level CNN
one hot encoding using numpy, sklearn, and keras. Created Date: 7 Jan 2019
Generic encoding of record types
This is my contribution to a competition on kaggle.com, where you have a dataset with 79 explanatory variables describing (almost) every aspect of c. 1500 residential homes in Ames, Iowa. The aim is to predict the final price of each home.
To predict whether booked appointment will be completed or it will be no show.
Deep Neural Networks like Single Layer Perceptron and Multi Layer Perceptron implementation using Tensorflow library on Datasets like MNIST and Naval Mine for categorical Classification. Saving and Restoring Tensorflow "Variables" weights for testing.
Basic ML using Sklearn to save/load a model, split training & test dataset, create dummy variables and one hot encoder
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