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| author | Navan Chauhan <navanchauhan@gmail.com> | 2020-01-29 12:03:17 +0530 |
|---|---|---|
| committer | Navan Chauhan <navanchauhan@gmail.com> | 2020-01-29 12:03:17 +0530 |
| commit | c61da5435eb6669c829d006c70a32fe8febae7a5 (patch) | |
| tree | e3b8816d3599869e66e959a5fabfe0d08eb2ea13 /posts | |
| parent | 32df66afceaeb8f558096c5676fd626cfe7358fd (diff) | |
Publish deploy 2020-01-29 12:03
Diffstat (limited to 'posts')
| -rw-r--r-- | posts/2019-12-10-TensorFlow-Model-Prediction/index 2.html | 23 | ||||
| -rw-r--r-- | posts/2019-12-22-Fake-News-Detector/index 2.html | 173 | ||||
| -rw-r--r-- | posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab/index 2.html | 9 | ||||
| -rw-r--r-- | posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal/index 2.html | 1 | ||||
| -rw-r--r-- | posts/hello-world/index 2.html | 1 | ||||
| -rw-r--r-- | posts/index 2.html | 1 |
6 files changed, 0 insertions, 208 deletions
diff --git a/posts/2019-12-10-TensorFlow-Model-Prediction/index 2.html b/posts/2019-12-10-TensorFlow-Model-Prediction/index 2.html deleted file mode 100644 index 3b71ea8..0000000 --- a/posts/2019-12-10-TensorFlow-Model-Prediction/index 2.html +++ /dev/null @@ -1,23 +0,0 @@ -<!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/2019-12-10-TensorFlow-Model-Prediction"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/2019-12-10-TensorFlow-Model-Prediction"/><meta name="og:url" content="https://navanchauhan.github.io/posts/2019-12-10-TensorFlow-Model-Prediction"/><title>Making Predictions using Image Classifier (TensorFlow) | Navan Chauhan</title><meta name="twitter:title" content="Making Predictions using Image Classifier (TensorFlow) | Navan Chauhan"/><meta name="og:title" content="Making Predictions using Image Classifier (TensorFlow) | Navan Chauhan"/><meta name="description" content="Making predictions for image classification models built using TensorFlow"/><meta name="twitter:description" content="Making predictions for image classification models built using TensorFlow"/><meta name="og:description" content="Making predictions for image classification models built using TensorFlow"/><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">🕑 1 minute read.</span><h1>Making Predictions using Image Classifier (TensorFlow)</h1><p><em>This was tested on TF 2.x and works as of 2019-12-10</em></p><p>If you want to understand how to make your own custom image classifier, please refer to my previous post.</p><p>If you followed my last post, then you created a model which took an image of dimensions 50x50 as an input.</p><p>First we import the following if we have not imported these before</p><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">cv2</span> -<span class="kn">import</span> <span class="nn">os</span> -</div> - -</code></pre><p>Then we read the file using OpenCV.</p><pre><code><div class="highlight"><span></span><span class="n">image</span><span class="o">=</span><span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">imagePath</span><span class="p">)</span> -</div> - -</code></pre><p>The cv2. imread() function returns a NumPy array representing the image. Therefore, we need to convert it before we can use it.</p><pre><code><div class="highlight"><span></span><span class="n">image_from_array</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="s1">'RGB'</span><span class="p">)</span> -</div> - -</code></pre><p>Then we resize the image</p><pre><code><div class="highlight"><span></span><span class="n">size_image</span> <span class="o">=</span> <span class="n">image_from_array</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">50</span><span class="p">,</span><span class="mi">50</span><span class="p">))</span> -</div> - -</code></pre><p>After this we create a batch consisting of only one image</p><pre><code><div class="highlight"><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">size_image</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> -</div> - -</code></pre><p>We then convert this uint8 datatype to a float32 datatype</p><pre><code><div class="highlight"><span></span><span class="n">img</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> -</div> - -</code></pre><p>Finally we make the prediction</p><pre><code><div class="highlight"><span></span><span class="k">print</span><span class="p">([</span><span class="s1">'Infected'</span><span class="p">,</span><span class="s1">'Uninfected'</span><span class="p">][</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">img</span><span class="p">))])</span> -</div> - -</code></pre><p><code>Infected</code></p></div><span>Tagged with: </span><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/codesnippet">code-snippet</a></li></ul><div id="disqus_thread"></div><script src="/assets/disqus.js"></script><noscript>Please enable JavaScript to view the comments</noscript></article></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/posts/2019-12-22-Fake-News-Detector/index 2.html b/posts/2019-12-22-Fake-News-Detector/index 2.html deleted file mode 100644 index a29cd6c..0000000 --- a/posts/2019-12-22-Fake-News-Detector/index 2.html +++ /dev/null @@ -1,173 +0,0 @@ -<!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> - -</code></pre><p>If you do not wish to train on GPU or are running it on your computer, you can ignore the last two lines</p><h3>Downloading the Dataset</h3><pre><code><div class="highlight"><span></span><span class="nt">!wget</span><span class="na"> -q "https</span><span class="p">:</span><span class="nc">//github.com/joolsa/fake_real_news_dataset/raw/master/fake_or_real_news.csv.zip"</span> -<span class="nt">!unzip</span><span class="na"> fake_or_real_news.csv.zip</span> -</div> - -</code></pre><h3>Model Creation</h3><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">turicreate</span> <span class="kn">as</span> <span class="nn">tc</span> -<span class="n">tc</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">set_num_gpus</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># If you do not wish to use GPUs, set it to 0</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="n">dataSFrame</span> <span class="o">=</span> <span class="n">tc</span><span class="o">.</span><span class="n">SFrame</span><span class="p">(</span><span class="s1">'fake_or_real_news.csv'</span><span class="p">)</span> -</div> - -</code></pre><p>The dataset contains a column named "X1", which is of no use to us. Therefore, we simply drop it</p><pre><code><div class="highlight"><span></span><span class="n">dataSFrame</span><span class="o">.</span><span class="n">remove_column</span><span class="p">(</span><span class="s1">'X1'</span><span class="p">)</span> -</div> - -</code></pre><h4>Splitting Dataset</h4><pre><code><div class="highlight"><span></span><span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">dataSFrame</span><span class="o">.</span><span class="n">random_split</span><span class="p">(</span><span class="o">.</span><span class="mi">9</span><span class="p">)</span> -</div> - -</code></pre><h4>Training</h4><pre><code><div class="highlight"><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">tc</span><span class="o">.</span><span class="n">text_classifier</span><span class="o">.</span><span class="n">create</span><span class="p">(</span> - <span class="n">dataset</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> - <span class="n">target</span><span class="o">=</span><span class="s1">'label'</span><span class="p">,</span> - <span class="n">features</span><span class="o">=</span><span class="p">[</span><span class="s1">'title'</span><span class="p">,</span><span class="s1">'text'</span><span class="p">]</span> -<span class="p">)</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="o">+-----------+----------+-----------+--------------+-------------------+---------------------+</span> -<span class="o">|</span> <span class="n">Iteration</span> <span class="o">|</span> <span class="n">Passes</span> <span class="o">|</span> <span class="n">Step</span> <span class="n">size</span> <span class="o">|</span> <span class="n">Elapsed</span> <span class="n">Time</span> <span class="o">|</span> <span class="n">Training</span> <span class="n">Accuracy</span> <span class="o">|</span> <span class="n">Validation</span> <span class="n">Accuracy</span> <span class="o">|</span> -<span class="o">+-----------+----------+-----------+--------------+-------------------+---------------------+</span> -<span class="o">|</span> <span class="mi">0</span> <span class="o">|</span> <span class="mi">2</span> <span class="o">|</span> <span class="mf">1.000000</span> <span class="o">|</span> <span class="mf">1.156349</span> <span class="o">|</span> <span class="mf">0.889680</span> <span class="o">|</span> <span class="mf">0.790036</span> <span class="o">|</span> -<span class="o">|</span> <span class="mi">1</span> <span class="o">|</span> <span class="mi">4</span> <span class="o">|</span> <span class="mf">1.000000</span> <span class="o">|</span> <span class="mf">1.359196</span> <span class="o">|</span> <span class="mf">0.985952</span> <span class="o">|</span> <span class="mf">0.918149</span> <span class="o">|</span> -<span class="o">|</span> <span class="mi">2</span> <span class="o">|</span> <span class="mi">6</span> <span class="o">|</span> <span class="mf">0.820091</span> <span class="o">|</span> <span class="mf">1.557205</span> <span class="o">|</span> <span class="mf">0.990260</span> <span class="o">|</span> <span class="mf">0.914591</span> <span class="o">|</span> -<span class="o">|</span> <span class="mi">3</span> <span class="o">|</span> <span class="mi">7</span> <span class="o">|</span> <span class="mf">1.000000</span> <span class="o">|</span> <span class="mf">1.684872</span> <span class="o">|</span> <span class="mf">0.998689</span> <span class="o">|</span> <span class="mf">0.925267</span> <span class="o">|</span> -<span class="o">|</span> <span class="mi">4</span> <span class="o">|</span> <span class="mi">8</span> <span class="o">|</span> <span class="mf">1.000000</span> <span class="o">|</span> <span class="mf">1.814194</span> <span class="o">|</span> <span class="mf">0.999063</span> <span class="o">|</span> <span class="mf">0.925267</span> <span class="o">|</span> -<span class="o">|</span> <span class="mi">9</span> <span class="o">|</span> <span class="mi">14</span> <span class="o">|</span> <span class="mf">1.000000</span> <span class="o">|</span> <span class="mf">2.507072</span> <span class="o">|</span> <span class="mf">1.000000</span> <span class="o">|</span> <span class="mf">0.911032</span> <span class="o">|</span> -<span class="o">+-----------+----------+-----------+--------------+-------------------+---------------------+</span> -</div> - -</code></pre><h3>Testing the Model</h3><pre><code><div class="highlight"><span></span><span class="n">est_predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test</span><span class="p">)</span> -<span class="n">accuracy</span> <span class="o">=</span> <span class="n">tc</span><span class="o">.</span><span class="n">evaluation</span><span class="o">.</span><span class="n">accuracy</span><span class="p">(</span><span class="n">test</span><span class="p">[</span><span class="s1">'label'</span><span class="p">],</span> <span class="n">test_predictions</span><span class="p">)</span> -<span class="k">print</span><span class="p">(</span><span class="n">f</span><span class="s1">'Topic classifier model has a testing accuracy of {accuracy*100}% '</span><span class="p">,</span> <span class="n">flush</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="n">Topic</span> <span class="n">classifier</span> <span class="n">model</span> <span class="n">has</span> <span class="n">a</span> <span class="n">testing</span> <span class="n">accuracy</span> <span class="n">of</span> <span class="mf">92.3076923076923</span><span class="o">%</span> -</div> - -</code></pre><p>We have just created our own Fake News Detection Model which has an accuracy of 92%!</p><pre><code><div class="highlight"><span></span><span class="n">example_text</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"title"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Middling ‘Rise Of Skywalker’ Review Leaves Fan On Fence About Whether To Threaten To Kill Critic"</span><span class="p">],</span> <span class="s2">"text"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"Expressing ambivalence toward the relatively balanced appraisal of the film, Star Wars fan Miles Ariely admitted Thursday that an online publication’s middling review of The Rise Of Skywalker had left him on the fence about whether he would still threaten to kill the critic who wrote it. “I’m really of two minds about this, because on the one hand, he said the new movie fails to live up to the original trilogy, which makes me at least want to throw a brick through his window with a note telling him to watch his back,” said Ariely, confirming he had already drafted an eight-page-long death threat to Stan Corimer of the website Screen-On Time, but had not yet decided whether to post it to the reviewer’s Facebook page. “On the other hand, though, he commended J.J. Abrams’ skillful pacing and faithfulness to George Lucas’ vision, which makes me wonder if I should just call the whole thing off. Now, I really don’t feel like camping outside his house for hours. Maybe I could go with a response that’s somewhere in between, like, threatening to kill his dog but not everyone in his whole family? I don’t know. This is a tough one.” At press time, sources reported that Ariely had resolved to wear his Ewok costume while he murdered the critic in his sleep."</span><span class="p">]}</span> -<span class="n">example_prediction</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classify</span><span class="p">(</span><span class="n">tc</span><span class="o">.</span><span class="n">SFrame</span><span class="p">(</span><span class="n">example_text</span><span class="p">))</span> -<span class="k">print</span><span class="p">(</span><span class="n">example_prediction</span><span class="p">,</span> <span class="n">flush</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span> -</div> - -</code></pre><pre><code><div class="highlight"><span></span><span class="o">+-------+--------------------+</span> -<span class="o">|</span> <span class="k">class</span> <span class="err">| </span><span class="nc">probability</span> <span class="o">|</span> -<span class="o">+-------+--------------------+</span> -<span class="o">|</span> <span class="n">FAKE</span> <span class="o">|</span> <span class="mf">0.9245648658345308</span> <span class="o">|</span> -<span class="o">+-------+--------------------+</span> -<span class="p">[</span><span class="mi">1</span> <span class="n">rows</span> <span class="n">x</span> <span class="mi">2</span> <span class="n">columns</span><span class="p">]</span> -</div> - -</code></pre><h3>Exporting the Model</h3><pre><code><div class="highlight"><span></span><span class="n">model_name</span> <span class="o">=</span> <span class="s1">'FakeNews'</span> -<span class="n">coreml_model_name</span> <span class="o">=</span> <span class="n">model_name</span> <span class="o">+</span> <span class="s1">'.mlmodel'</span> -<span class="n">exportedModel</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">export_coreml</span><span class="p">(</span><span class="n">coreml_model_name</span><span class="p">)</span> -</div> - -</code></pre><p><strong>Note: To download files from Google Volab, simply click on the files section in the sidebar, right click on filename and then click on downlaod</strong></p><p><a href="https://colab.research.google.com/drive/1onMXGkhA__X2aOFdsoVL-6HQBsWQhOP4">Link to Colab Notebook</a></p><h2>Building the App using SwiftUI</h2><h3>Initial Setup</h3><p>First we create a single view app (make sure you check the use SwiftUI button)</p><p>Then we copy our .mlmodel file to our project (Just drag and drop the file in the XCode Files Sidebar)</p><p>Our ML Model does not take a string directly as an input, rather it takes bag of words as an input. DescriptionThe bag-of-words model is a simplifying representation used in NLP, in this text is represented as a bag of words, without any regatd of grammar or order, but noting multiplicity</p><p>We define our bag of words function</p><pre><code><div class="highlight"><span></span><span class="kd">func</span> <span class="nf">bow</span><span class="p">(</span><span class="n">text</span><span class="p">:</span> <span class="nb">String</span><span class="p">)</span> <span class="p">-></span> <span class="p">[</span><span class="nb">String</span><span class="p">:</span> <span class="nb">Double</span><span class="p">]</span> <span class="p">{</span> - <span class="kd">var</span> <span class="nv">bagOfWords</span> <span class="p">=</span> <span class="p">[</span><span class="nb">String</span><span class="p">:</span> <span class="nb">Double</span><span class="p">]()</span> - - <span class="kd">let</span> <span class="nv">tagger</span> <span class="p">=</span> <span class="bp">NSLinguisticTagger</span><span class="p">(</span><span class="n">tagSchemes</span><span class="p">:</span> <span class="p">[.</span><span class="n">tokenType</span><span class="p">],</span> <span class="n">options</span><span class="p">:</span> <span class="mi">0</span><span class="p">)</span> - <span class="kd">let</span> <span class="nv">range</span> <span class="p">=</span> <span class="n">NSRange</span><span class="p">(</span><span class="n">location</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="n">length</span><span class="p">:</span> <span class="n">text</span><span class="p">.</span><span class="n">utf16</span><span class="p">.</span><span class="bp">count</span><span class="p">)</span> - <span class="kd">let</span> <span class="nv">options</span><span class="p">:</span> <span class="bp">NSLinguisticTagger</span><span class="p">.</span><span class="n">Options</span> <span class="p">=</span> <span class="p">[.</span><span class="n">omitPunctuation</span><span class="p">,</span> <span class="p">.</span><span class="n">omitWhitespace</span><span class="p">]</span> - <span class="n">tagger</span><span class="p">.</span><span class="n">string</span> <span class="p">=</span> <span class="n">text</span> - - <span class="n">tagger</span><span class="p">.</span><span class="n">enumerateTags</span><span class="p">(</span><span class="k">in</span><span class="p">:</span> <span class="n">range</span><span class="p">,</span> <span class="n">unit</span><span class="p">:</span> <span class="p">.</span><span class="n">word</span><span class="p">,</span> <span class="n">scheme</span><span class="p">:</span> <span class="p">.</span><span class="n">tokenType</span><span class="p">,</span> <span class="n">options</span><span class="p">:</span> <span class="n">options</span><span class="p">)</span> <span class="p">{</span> <span class="kc">_</span><span class="p">,</span> <span class="n">tokenRange</span><span class="p">,</span> <span class="kc">_</span> <span class="k">in</span> - <span class="kd">let</span> <span class="nv">word</span> <span class="p">=</span> <span class="p">(</span><span class="n">text</span> <span class="k">as</span> <span class="bp">NSString</span><span class="p">).</span><span class="n">substring</span><span class="p">(</span><span class="n">with</span><span class="p">:</span> <span class="n">tokenRange</span><span class="p">)</span> - <span class="k">if</span> <span class="n">bagOfWords</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">!=</span> <span class="kc">nil</span> <span class="p">{</span> - <span class="n">bagOfWords</span><span class="p">[</span><span class="n">word</span><span class="p">]</span><span class="o">!</span> <span class="o">+=</span> <span class="mi">1</span> - <span class="p">}</span> <span class="k">else</span> <span class="p">{</span> - <span class="n">bagOfWords</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="p">=</span> <span class="mi">1</span> - <span class="p">}</span> - <span class="p">}</span> - - <span class="k">return</span> <span class="n">bagOfWords</span> - <span class="p">}</span> -</div> - -</code></pre><p>We also declare our variables</p><pre><code><div class="highlight"><span></span><span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">title</span><span class="p">:</span> <span class="nb">String</span> <span class="p">=</span> <span class="s">""</span> -<span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">headline</span><span class="p">:</span> <span class="nb">String</span> <span class="p">=</span> <span class="s">""</span> -<span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">alertTitle</span> <span class="p">=</span> <span class="s">""</span> -<span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">alertText</span> <span class="p">=</span> <span class="s">""</span> -<span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">showingAlert</span> <span class="p">=</span> <span class="kc">false</span> -</div> - -</code></pre><p>Finally, we implement a simple function which reads the two text fields, creates their bag of words representation and displays an alert with the appropriate result</p><p><strong>Complete Code</strong></p><pre><code><div class="highlight"><span></span><span class="kd">import</span> <span class="nc">SwiftUI</span> - -<span class="kd">struct</span> <span class="nc">ContentView</span><span class="p">:</span> <span class="n">View</span> <span class="p">{</span> - <span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">title</span><span class="p">:</span> <span class="nb">String</span> <span class="p">=</span> <span class="s">""</span> - <span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">headline</span><span class="p">:</span> <span class="nb">String</span> <span class="p">=</span> <span class="s">""</span> - - <span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">alertTitle</span> <span class="p">=</span> <span class="s">""</span> - <span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">alertText</span> <span class="p">=</span> <span class="s">""</span> - <span class="p">@</span><span class="n">State</span> <span class="kd">private</span> <span class="kd">var</span> <span class="nv">showingAlert</span> <span class="p">=</span> <span class="kc">false</span> - - <span class="kd">var</span> <span class="nv">body</span><span class="p">:</span> <span class="n">some</span> <span class="n">View</span> <span class="p">{</span> - <span class="n">NavigationView</span> <span class="p">{</span> - <span class="n">VStack</span><span class="p">(</span><span class="n">alignment</span><span class="p">:</span> <span class="p">.</span><span class="n">leading</span><span class="p">)</span> <span class="p">{</span> - <span class="n">Text</span><span class="p">(</span><span class="s">"Headline"</span><span class="p">).</span><span class="n">font</span><span class="p">(.</span><span class="n">headline</span><span class="p">)</span> - <span class="n">TextField</span><span class="p">(</span><span class="s">"Please Enter Headline"</span><span class="p">,</span> <span class="n">text</span><span class="p">:</span> <span class="err">$</span><span class="n">title</span><span class="p">)</span> - <span class="p">.</span><span class="n">lineLimit</span><span class="p">(</span><span class="kc">nil</span><span class="p">)</span> - <span class="n">Text</span><span class="p">(</span><span class="s">"Body"</span><span class="p">).</span><span class="n">font</span><span class="p">(.</span><span class="n">headline</span><span class="p">)</span> - <span class="n">TextField</span><span class="p">(</span><span class="s">"Please Enter the content"</span><span class="p">,</span> <span class="n">text</span><span class="p">:</span> <span class="err">$</span><span class="n">headline</span><span class="p">)</span> - <span class="p">.</span><span class="n">lineLimit</span><span class="p">(</span><span class="kc">nil</span><span class="p">)</span> - <span class="p">}</span> - <span class="p">.</span><span class="n">navigationBarTitle</span><span class="p">(</span><span class="s">"Fake News Checker"</span><span class="p">)</span> - <span class="p">.</span><span class="n">navigationBarItems</span><span class="p">(</span><span class="n">trailing</span><span class="p">:</span> - <span class="n">Button</span><span class="p">(</span><span class="n">action</span><span class="p">:</span> <span class="n">classifyFakeNews</span><span class="p">)</span> <span class="p">{</span> - <span class="n">Text</span><span class="p">(</span><span class="s">"Check"</span><span class="p">)</span> - <span class="p">})</span> - <span class="p">.</span><span class="n">padding</span><span class="p">()</span> - <span class="p">.</span><span class="n">alert</span><span class="p">(</span><span class="n">isPresented</span><span class="p">:</span> <span class="err">$</span><span class="n">showingAlert</span><span class="p">){</span> - <span class="n">Alert</span><span class="p">(</span><span class="n">title</span><span class="p">:</span> <span class="n">Text</span><span class="p">(</span><span class="n">alertTitle</span><span class="p">),</span> <span class="n">message</span><span class="p">:</span> <span class="n">Text</span><span class="p">(</span><span class="n">alertText</span><span class="p">),</span> <span class="n">dismissButton</span><span class="p">:</span> <span class="p">.</span><span class="k">default</span><span class="p">(</span><span class="n">Text</span><span class="p">(</span><span class="s">"OK"</span><span class="p">)))</span> - <span class="p">}</span> - <span class="p">}</span> - - <span class="p">}</span> - - <span class="kd">func</span> <span class="nf">classifyFakeNews</span><span class="p">(){</span> - <span class="kd">let</span> <span class="nv">model</span> <span class="p">=</span> <span class="n">FakeNews</span><span class="p">()</span> - <span class="kd">let</span> <span class="nv">myTitle</span> <span class="p">=</span> <span class="n">bow</span><span class="p">(</span><span class="n">text</span><span class="p">:</span> <span class="n">title</span><span class="p">)</span> - <span class="kd">let</span> <span class="nv">myText</span> <span class="p">=</span> <span class="n">bow</span><span class="p">(</span><span class="n">text</span><span class="p">:</span> <span class="n">headline</span><span class="p">)</span> - <span class="k">do</span> <span class="p">{</span> - <span class="kd">let</span> <span class="nv">prediction</span> <span class="p">=</span> <span class="k">try</span> <span class="n">model</span><span class="p">.</span><span class="n">prediction</span><span class="p">(</span><span class="n">title</span><span class="p">:</span> <span class="n">myTitle</span><span class="p">,</span> <span class="n">text</span><span class="p">:</span> <span class="n">myText</span><span class="p">)</span> - <span class="n">alertTitle</span> <span class="p">=</span> <span class="n">prediction</span><span class="p">.</span><span class="n">label</span> - <span class="n">alertText</span> <span class="p">=</span> <span class="s">"It is likely that this piece of news is </span><span class="si">\(</span><span class="n">prediction</span><span class="p">.</span><span class="n">label</span><span class="p">.</span><span class="n">lowercased</span><span class="si">())</span><span class="s">."</span> - <span class="bp">print</span><span class="p">(</span><span class="n">alertText</span><span class="p">)</span> - <span class="p">}</span> <span class="k">catch</span> <span class="p">{</span> - <span class="n">alertTitle</span> <span class="p">=</span> <span class="s">"Error"</span> - <span class="n">alertText</span> <span class="p">=</span> <span class="s">"Sorry, could not classify if the input news was fake or not."</span> - <span class="p">}</span> - - <span class="n">showingAlert</span> <span class="p">=</span> <span class="kc">true</span> - <span class="p">}</span> - <span class="kd">func</span> <span class="nf">bow</span><span class="p">(</span><span class="n">text</span><span class="p">:</span> <span class="nb">String</span><span class="p">)</span> <span class="p">-></span> <span class="p">[</span><span class="nb">String</span><span class="p">:</span> <span class="nb">Double</span><span class="p">]</span> <span class="p">{</span> - <span class="kd">var</span> <span class="nv">bagOfWords</span> <span class="p">=</span> <span class="p">[</span><span class="nb">String</span><span class="p">:</span> <span class="nb">Double</span><span class="p">]()</span> - - <span class="kd">let</span> <span class="nv">tagger</span> <span class="p">=</span> <span class="bp">NSLinguisticTagger</span><span class="p">(</span><span class="n">tagSchemes</span><span class="p">:</span> <span class="p">[.</span><span class="n">tokenType</span><span class="p">],</span> <span class="n">options</span><span class="p">:</span> <span class="mi">0</span><span class="p">)</span> - <span class="kd">let</span> <span class="nv">range</span> <span class="p">=</span> <span class="n">NSRange</span><span class="p">(</span><span class="n">location</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="n">length</span><span class="p">:</span> <span class="n">text</span><span class="p">.</span><span class="n">utf16</span><span class="p">.</span><span class="bp">count</span><span class="p">)</span> - <span class="kd">let</span> <span class="nv">options</span><span class="p">:</span> <span class="bp">NSLinguisticTagger</span><span class="p">.</span><span class="n">Options</span> <span class="p">=</span> <span class="p">[.</span><span class="n">omitPunctuation</span><span class="p">,</span> <span class="p">.</span><span class="n">omitWhitespace</span><span class="p">]</span> - <span class="n">tagger</span><span class="p">.</span><span class="n">string</span> <span class="p">=</span> <span class="n">text</span> - - <span class="n">tagger</span><span class="p">.</span><span class="n">enumerateTags</span><span class="p">(</span><span class="k">in</span><span class="p">:</span> <span class="n">range</span><span class="p">,</span> <span class="n">unit</span><span class="p">:</span> <span class="p">.</span><span class="n">word</span><span class="p">,</span> <span class="n">scheme</span><span class="p">:</span> <span class="p">.</span><span class="n">tokenType</span><span class="p">,</span> <span class="n">options</span><span class="p">:</span> <span class="n">options</span><span class="p">)</span> <span class="p">{</span> <span class="kc">_</span><span class="p">,</span> <span class="n">tokenRange</span><span class="p">,</span> <span class="kc">_</span> <span class="k">in</span> - <span class="kd">let</span> <span class="nv">word</span> <span class="p">=</span> <span class="p">(</span><span class="n">text</span> <span class="k">as</span> <span class="bp">NSString</span><span class="p">).</span><span class="n">substring</span><span class="p">(</span><span class="n">with</span><span class="p">:</span> <span class="n">tokenRange</span><span class="p">)</span> - <span class="k">if</span> <span class="n">bagOfWords</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">!=</span> <span class="kc">nil</span> <span class="p">{</span> - <span class="n">bagOfWords</span><span class="p">[</span><span class="n">word</span><span class="p">]</span><span class="o">!</span> <span class="o">+=</span> <span class="mi">1</span> - <span class="p">}</span> <span class="k">else</span> <span class="p">{</span> - <span class="n">bagOfWords</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="p">=</span> <span class="mi">1</span> - <span class="p">}</span> - <span class="p">}</span> - - <span class="k">return</span> <span class="n">bagOfWords</span> - <span class="p">}</span> -<span class="p">}</span> - -<span class="kd">struct</span> <span class="nc">ContentView_Previews</span><span class="p">:</span> <span class="n">PreviewProvider</span> <span class="p">{</span> - <span class="kd">static</span> <span class="kd">var</span> <span class="nv">previews</span><span class="p">:</span> <span class="n">some</span> <span class="n">View</span> <span class="p">{</span> - <span class="n">ContentView</span><span class="p">()</span> - <span class="p">}</span> -<span class="p">}</span> -</div> - -</code></pre></div><span>Tagged with: </span><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/colab">colab</a></li><li><a href="/tags/swiftui">swiftUI</a></li><li><a href="/tags/turicreate">turicreate</a></li></ul><div id="disqus_thread"></div><script src="/assets/disqus.js"></script><noscript>Please enable JavaScript to view the comments</noscript></article></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab/index 2.html b/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab/index 2.html deleted file mode 100644 index ab122f4..0000000 --- a/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab/index 2.html +++ /dev/null @@ -1,9 +0,0 @@ -<!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/2020-01-15-Setting-up-Kaggle-to-use-with-Colab"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab"/><meta name="og:url" content="https://navanchauhan.github.io/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab"/><title>Setting up Kaggle to use with Google Colab | Navan Chauhan</title><meta name="twitter:title" content="Setting up Kaggle to use with Google Colab | Navan Chauhan"/><meta name="og:title" content="Setting up Kaggle to use with Google Colab | Navan Chauhan"/><meta name="description" content="Tutorial on setting up kaggle, to use with Google Colab"/><meta name="twitter:description" content="Tutorial on setting up kaggle, to use with Google Colab"/><meta name="og:description" content="Tutorial on setting up kaggle, to use with Google Colab"/><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">🕑 1 minute read.</span><h1>Setting up Kaggle to use with Google Colab</h1><p><em>In order to be able to access Kaggle Datasets, you will need to have an account on Kaggle (which is Free)</em></p><h2>Grabbing Our Tokens</h2><h3>Go to Kaggle</h3><img src="/assets/posts/kaggle-colab/ss1.png" alt=""Homepage""/><h3>Click on your User Profile and Click on My Account</h3><img src="/assets/posts/kaggle-colab/ss2.png" alt=""Account""/><h3>Scroll Down untill you see Create New API Token</h3><img src="/assets/posts/kaggle-colab/ss3.png"/><h3>This will download your token as a JSON file</h3><img src="/assets/posts/kaggle-colab/ss4.png"/><p>Copy the File to the root folder of your Google Drive</p><h2>Setting up Colab</h2><h3>Mounting Google Drive</h3><pre><code><div class="highlight"><span></span><span class="kn">import</span> <span class="nn">os</span> -<span class="kn">from</span> <span class="nn">google.colab</span> <span class="kn">import</span> <span class="n">drive</span> -<span class="n">drive</span><span class="o">.</span><span class="n">mount</span><span class="p">(</span><span class="s1">'/content/drive'</span><span class="p">)</span> -</div> - -</code></pre><p>After this click on the URL in the output section, login and then paste the Auth Code</p><h3>Configuring Kaggle</h3><pre><code><div class="highlight"><span></span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'KAGGLE_CONFIG_DIR'</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"/content/drive/My Drive/"</span> -</div> - -</code></pre><p>Voila! You can now download kaggel datasets</p></div><span>Tagged with: </span><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/colab">colab</a></li><li><a href="/tags/turicreate">turicreate</a></li><li><a href="/tags/kaggle">kaggle</a></li></ul><div id="disqus_thread"></div><script src="/assets/disqus.js"></script><noscript>Please enable JavaScript to view the comments</noscript></article></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal/index 2.html b/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal/index 2.html deleted file mode 100644 index 4f56a6d..0000000 --- a/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal/index 2.html +++ /dev/null @@ -1 +0,0 @@ -<!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/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal"/><meta name="og:url" content="https://navanchauhan.github.io/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal"/><title>How to setup Bluetooth on a Raspberry Pi | Navan Chauhan</title><meta name="twitter:title" content="How to setup Bluetooth on a Raspberry Pi | Navan Chauhan"/><meta name="og:title" content="How to setup Bluetooth on a Raspberry Pi | Navan Chauhan"/><meta name="description" content="Connecting to Bluetooth Devices using terminal, tested on Raspberry Pi Zero W"/><meta name="twitter:description" content="Connecting to Bluetooth Devices using terminal, tested on Raspberry Pi Zero W"/><meta name="og:description" content="Connecting to Bluetooth Devices using terminal, tested on Raspberry Pi Zero W"/><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">🕑 0 minute read.</span><h1>How to setup Bluetooth on a Raspberry Pi</h1><p><em>This was tested on a Raspberry Pi Zero W</em></p><h2>Enter in the Bluetooth Mode</h2><p><code>pi@raspberrypi:~ $ bluetoothctl</code></p><p><code>[bluetooth]# agent on</code></p><p><code>[bluetooth]# default-agent</code></p><p><code>[bluetooth]# scan on</code></p><h2>To Pair</h2><p>While being in bluetooth mode</p><p><code>[bluetooth]# pair XX:XX:XX:XX:XX:XX</code></p><p>To Exit out of bluetoothctl anytime, just type exit</p></div><span>Tagged with: </span><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/raspberrypi">raspberry-pi</a></li><li><a href="/tags/linux">linux</a></li></ul><div id="disqus_thread"></div><script src="/assets/disqus.js"></script><noscript>Please enable JavaScript to view the comments</noscript></article></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/posts/hello-world/index 2.html b/posts/hello-world/index 2.html deleted file mode 100644 index e819ba1..0000000 --- a/posts/hello-world/index 2.html +++ /dev/null @@ -1 +0,0 @@ -<!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/hello-world"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts/hello-world"/><meta name="og:url" content="https://navanchauhan.github.io/posts/hello-world"/><title>Hello World | Navan Chauhan</title><meta name="twitter:title" content="Hello World | Navan Chauhan"/><meta name="og:title" content="Hello World | Navan Chauhan"/><meta name="description" content="My first post."/><meta name="twitter:description" content="My first post."/><meta name="og:description" content="My first post."/><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">🕑 0 minute read.</span><h1>Hello World</h1><p><strong>Why a Hello World post?</strong></p><p>Just re-did the entire website using Publish (Publish by John Sundell). So, a new hello world post :)</p></div><span>Tagged with: </span><ul class="tag-list"><li><a href="/tags/helloworld">hello-world</a></li><li><a href="/tags/article">article</a></li></ul><div id="disqus_thread"></div><script src="/assets/disqus.js"></script><noscript>Please enable JavaScript to view the comments</noscript></article></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
\ No newline at end of file diff --git a/posts/index 2.html b/posts/index 2.html deleted file mode 100644 index 9ac470f..0000000 --- a/posts/index 2.html +++ /dev/null @@ -1 +0,0 @@ -<!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"/><meta name="twitter:url" content="https://navanchauhan.github.io/posts"/><meta name="og:url" content="https://navanchauhan.github.io/posts"/><title>Posts | Navan Chauhan</title><meta name="twitter:title" content="Posts | Navan Chauhan"/><meta name="og:title" content="Posts | Navan Chauhan"/><meta name="description" content="Welcome to my personal fragment of the internet."/><meta name="twitter:description" content="Welcome to my personal fragment of the internet."/><meta name="og:description" content="Welcome to my personal fragment of the internet."/><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><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"><h1>Posts</h1><p>Tips, tricks and tutorials which I think might be useful.</p><ul class="item-list"><li><article><h1><a href="/posts/2019-12-08-Image-Classifier-Tensorflow">Creating a Custom Image Classifier using Tensorflow 2.x and Keras for Detecting Malaria</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 4 minute read.</span><p>Tutorial on creating an image classifier model using TensorFlow which detects malaria</p></article></li><li><article><h1><a href="/posts/2019-12-08-Splitting-Zips">Splitting ZIPs into Multiple Parts</a></h1><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li></ul><span>🕑 0 minute read.</span><p>Short code snippet for splitting zips.</p></article></li><li><article><h1><a href="/posts/2019-12-10-TensorFlow-Model-Prediction">Making Predictions using Image Classifier (TensorFlow)</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/codesnippet">code-snippet</a></li></ul><span>🕑 1 minute read.</span><p>Making predictions for image classification models built using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-16-TensorFlow-Polynomial-Regression">Polynomial Regression Using TensorFlow</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/tensorflow">tensorflow</a></li><li><a href="/tags/colab">colab</a></li></ul><span>🕑 16 minute read.</span><p>Polynomial regression using TensorFlow</p></article></li><li><article><h1><a href="/posts/2019-12-22-Fake-News-Detector">Building a Fake News Detector with Turicreate</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/colab">colab</a></li><li><a href="/tags/swiftui">swiftUI</a></li><li><a href="/tags/turicreate">turicreate</a></li></ul><span>🕑 6 minute read.</span><p>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</p></article></li><li><article><h1><a href="/posts/2020-01-14-Converting-between-PIL-NumPy">Converting between image and NumPy array</a></h1><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li></ul><span>🕑 0 minute read.</span><p>Short code snippet for converting between PIL image and NumPy arrays.</p></article></li><li><article><h1><a href="/posts/2020-01-15-Setting-up-Kaggle-to-use-with-Colab">Setting up Kaggle to use with Google Colab</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/colab">colab</a></li><li><a href="/tags/turicreate">turicreate</a></li><li><a href="/tags/kaggle">kaggle</a></li></ul><span>🕑 1 minute read.</span><p>Tutorial on setting up kaggle, to use with Google Colab</p></article></li><li><article><h1><a href="/posts/2020-01-16-Image-Classifier-Using-Turicreate">Creating a Custom Image Classifier using Turicreate to detect Smoke and Fire</a></h1><ul class="tag-list"><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/colab">colab</a></li><li><a href="/tags/turicreate">turicreate</a></li></ul><span>🕑 6 minute read.</span><p>Tutorial on creating a custom Image Classifier using Turicreate and a dataset from Kaggle</p></article></li><li><article><h1><a href="/posts/2020-01-19-Connect-To-Bluetooth-Devices-Linux-Terminal">How to setup Bluetooth on a Raspberry Pi</a></h1><ul class="tag-list"><li><a href="/tags/codesnippet">code-snippet</a></li><li><a href="/tags/tutorial">tutorial</a></li><li><a href="/tags/raspberrypi">raspberry-pi</a></li><li><a href="/tags/linux">linux</a></li></ul><span>🕑 0 minute read.</span><p>Connecting to Bluetooth Devices using terminal, tested on Raspberry Pi Zero W</p></article></li><li><article><h1><a href="/posts/2020-01-24-experiments">Experiments</a></h1><ul class="tag-list"><li><a href="/tags/experiment">experiment</a></li></ul><span>🕑 0 minute read.</span><p>Just a markdown file for all experiments related to the website</p></article></li><li><article><h1><a href="/posts/hello-world">Hello World</a></h1><ul class="tag-list"><li><a href="/tags/helloworld">hello-world</a></li><li><a href="/tags/article">article</a></li></ul><span>🕑 0 minute read.</span><p>My first post.</p></article></li></ul></div><footer><p>Made with ❤️ using <a href="https://github.com/johnsundell/publish">Publish</a></p><p><a href="/feed.rss">RSS feed</a></p></footer></body></html>
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