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@naruarjun
naruarjun / seres.ipynb
Created Dec 4, 2018
SE-Resnet Implementation
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View gist:50265b873104c88c533777937b3b10bc
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TASK1\n",
"So this is a classification model for Telco-Customer-Churn. So basically we have to predict whether churn is yes or no. The model is a simple linear SVM and achieved a accuracy of 75% on the test set."
]
View gist:da5d24f63dee7e41945e3c04b9d8b205
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TASK1\n",
"So this is a classification model for Telco-Customer-Churn. So basically we have to predict whether churn is yes or no. The model is a simple linear SVM and achieved a accuracy of 75% on the test set."
]
},
View gist:0aa919795288b6db9a92e69dd5db59f2
This file has been truncated, but you can view the full file.
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TASK1\n",
"So this is a classification model for Telco-Customer-Churn. So basically we have to predict whether churn is yes or no. The model is a simple linear SVM and achieved a accuracy of 100% on the test set."
]
View gist:8cf4f4924d28c0e1b65ad85e0fe08095
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pretrained Classification Model\n",
"This is a pretrained classification model to classify the natural images dataset. The pretrained network used is InceptionV3 and the deep learning framework used is keras. This model achieved a validation accuracy of about 98.32%. The Model is basically the InceptionV3 architecture with two dense layers on top of it. The output of the following cell contains the entire architecture of the network summarised. I manually divided the data into two folders. Natural_images contained my training data and natural_images_validation contains my validation data"
]
},
@naruarjun
naruarjun / main_classifier.ipynb
Created Aug 24, 2018
MAIN CLASSIFIER TRAINED FROM SCRATCH
View main_classifier.ipynb
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