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| author | Navan Chauhan <navanchauhan@gmail.com> | 2020-01-18 20:15:32 +0530 |
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| committer | Navan Chauhan <navanchauhan@gmail.com> | 2020-01-18 20:15:32 +0530 |
| commit | aef09ef3f7d0fe16de6d70bc64d49123ccac6741 (patch) | |
| tree | 612de2313957477ae613fda5d196bd8001830f96 /posts/2019-12-22-Fake-News-Detector | |
| parent | 177b2a4e44371b4c91f61b53961b390f9be83d15 (diff) | |
Publish deploy 2020-01-18 20:15
Diffstat (limited to 'posts/2019-12-22-Fake-News-Detector')
| -rw-r--r-- | posts/2019-12-22-Fake-News-Detector/index.html | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/posts/2019-12-22-Fake-News-Detector/index.html b/posts/2019-12-22-Fake-News-Detector/index.html index 5b46c95..c47ec61 100644 --- a/posts/2019-12-22-Fake-News-Detector/index.html +++ b/posts/2019-12-22-Fake-News-Detector/index.html @@ -1,4 +1,4 @@ -<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/posts/2019-12-22-Fake-News-Detector"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/2019-12-22-Fake-News-Detector"/><meta name="og:url" content="https://navanchauhan.github.io/posts/2019-12-22-Fake-News-Detector"/><title>Building a Fake News Detector with Turicreate | Navan Chauhan</title><meta name="twitter:title" content="Building a Fake News Detector with Turicreate | Navan Chauhan"/><meta name="og:title" content="Building a Fake News Detector with Turicreate | Navan Chauhan"/><meta name="description" content="In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app"/><meta name="twitter:description" content="In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app"/><meta name="og:description" content="In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app"/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="/about">About Me</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 6 minute read.</span><h1>Building a Fake News Detector with Turicreate</h1><p><strong>In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app</strong></p><p>Note: These commands are written as if you are running a jupyter notebook.</p><h2>Building the Machine Learning Model</h2><h3>Data Gathering</h3><p>To build a classifier, you need a lot of data. George McIntire (GH: @joolsa) has created a wonderful dataset containing the headline, body and wheter it is fake or real. Whenever you are looking for a dataset, always try searching on Kaggle and GitHub before you start building your own</p><h3>Dependencies</h3><p>I used a Google Colab instance for training my model. If you also plan on using Google Colab then I reccomend choosing a GPU Instance (It is Free) This allows you to train the model on the GPU. Turicreat is built on top of Apache's MXNet Framework, for us to use GPU we need to install a CUDA compatible MXNet package.</p><pre><code><div class="highlight"><span></span><span class="nt">!pip</span><span class="na"> install turicreate</span> +<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"/><meta name="og:site_name" content="Navan Chauhan"/><link rel="canonical" href="https://navanchauhan.github.io/posts/2019-12-22-Fake-News-Detector"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/2019-12-22-Fake-News-Detector"/><meta name="og:url" content="https://navanchauhan.github.io/posts/2019-12-22-Fake-News-Detector"/><title>Building a Fake News Detector with Turicreate | Navan Chauhan</title><meta name="twitter:title" content="Building a Fake News Detector with Turicreate | Navan Chauhan"/><meta name="og:title" content="Building a Fake News Detector with Turicreate | Navan Chauhan"/><meta name="description" content="In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app"/><meta name="twitter:description" content="In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app"/><meta name="og:description" content="In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app"/><meta name="twitter:card" content="summary"/><link rel="stylesheet" href="/styles.css" type="text/css"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><link rel="shortcut icon" href="/images/favicon.png" type="image/png"/><link rel="alternate" href="/feed.rss" type="application/rss+xml" title="Subscribe to Navan Chauhan"/><meta name="twitter:image" content="https://navanchauhan.github.io/images/logo.png"/><meta name="og:image" content="https://navanchauhan.github.io/images/logo.png"/></head><head><script src="https://www.googletagmanager.com/gtag/js?id=UA-108635191-1v"></script><script>window.dataLayer = window.dataLayer || [];function gtag(){dataLayer.push(arguments);}gtag('js', new Date());gtag('config', 'UA-108635191-1');</script></head><body class="item-page"><header><div class="wrapper"><a class="site-name" href="/">Navan Chauhan</a><nav><ul><li><a href="/about">About Me</a></li><li><a class="selected" href="/posts">Posts</a></li><li><a href="/publications">Publications</a></li><li><a href="./assets/résumé.pdf">Résumé</a></li><li><a href="https://navanchauhan.github.io/repo">Repo</a></li></ul></nav></div></header><div class="wrapper"><article><div class="content"><span class="reading-time">🕑 6 minute read.</span><h1>Building a Fake News Detector with Turicreate</h1><p><strong>In this tutorial we will build a fake news detecting app from scratch, using Turicreate for the machine learning model and SwiftUI for building the app</strong></p><p>Note: These commands are written as if you are running a jupyter notebook.</p><h2>Building the Machine Learning Model</h2><h3>Data Gathering</h3><p>To build a classifier, you need a lot of data. George McIntire (GH: @joolsa) has created a wonderful dataset containing the headline, body and wheter it is fake or real. Whenever you are looking for a dataset, always try searching on Kaggle and GitHub before you start building your own</p><h3>Dependencies</h3><p>I used a Google Colab instance for training my model. If you also plan on using Google Colab then I reccomend choosing a GPU Instance (It is Free) This allows you to train the model on the GPU. Turicreat is built on top of Apache's MXNet Framework, for us to use GPU we need to install a CUDA compatible MXNet package.</p><pre><code><div class="highlight"><span></span><span class="nt">!pip</span><span class="na"> install turicreate</span> <span class="na">!pip uninstall -y mxnet</span> <span class="na">!pip install mxnet-cu100==1.4.0.post0</span> </div> |