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Added new material:
1. Doodle classifier 2. Linear Regression 3. K-means Clustering 4. SVM Regression Updated existing material with better explanation: 1. Sentiment Analysis
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‎Algolyzer/study/templates/study/doodle_classifier.html‎

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{% extends "base.html" %}
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{% block content %}
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<div class="max-w-5xl mx-auto py-5 max-sm:p-4">
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<h1 class="max-sm:text-4xl max-sm:my-6 text-5xl my-12">
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A Beginner's Guide to <span class="text-primary font-bold font-poppins">Doodle Classification</span>
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</h1>
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<h2 class="max-sm:text-2xl text-3xl my-5 text-primary font-bold">Introduction</h2>
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<p class="max-sm:text-lg text-xl">
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Doodle classification is a type of image classification that predicts which number (0-9) was drawn by a user.
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This is done using a deep learning model trained on the MNIST dataset—a famous dataset of handwritten digits.
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</p>
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<p class="max-sm:text-lg text-xl mt-3">
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In this project, we're using a pre-trained model from Hugging Face called <code>farleyknight/mnist-digit-classification-2022-09-04</code>,
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which is specifically trained to recognize digits from rough sketches or "doodles."
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</p>
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<h2 class="max-sm:text-2xl text-3xl my-5 text-primary font-bold">How Does It Work?</h2>
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<ol class="list-decimal list-inside max-sm:text-lg text-xl">
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<li>The user draws a digit on a canvas in the browser.</li>
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<li>The drawing is converted to a base64 PNG image.</li>
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<li>This image is sent to the server and saved temporarily.</li>
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<li>The image is passed through a deep learning model.</li>
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<li>The model outputs the most likely digit with a confidence score.</li>
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</ol>
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<h2 class="max-sm:text-2xl text-3xl my-5 text-primary font-bold">Step-by-Step Breakdown</h2>
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<h3 class="max-sm:text-xl text-2xl my-4 text-secondary font-bold">Step 1: Capture the Drawing</h3>
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<p class="max-sm:text-lg text-xl">
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We use an HTML canvas where users draw their digit. The drawing is captured as a base64-encoded image string.
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</p>
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<div class="mockup-code mt-2">
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<pre><code>image_data = request.POST.get("image_data")</code></pre>
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</div>
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<h3 class="max-sm:text-xl text-2xl my-4 text-secondary font-bold">Step 2: Save the Drawing as an Image</h3>
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<p class="max-sm:text-lg text-xl">
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The base64 string is decoded and saved as a PNG image, so that it can be read like a normal image file.
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</p>
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<div class="mockup-code mt-2">
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<pre><code>format, imgstr = image_data.split(";base64,")
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ext = format.split("/")[-1]
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image_file = ContentFile(base64.b64decode(imgstr), name=f"doodle.{ext}")</code></pre>
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</div>
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<h3 class="max-sm:text-xl text-2xl my-4 text-secondary font-bold">Step 3: Run the Image through the Classifier</h3>
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<p class="max-sm:text-lg text-xl">
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The image is passed into a pre-trained model that analyzes the pixel patterns and predicts which digit was drawn.
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</p>
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<div class="mockup-code mt-2">
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<pre><code>image = Image.open(task.input_image.path)
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result = mnist_classifier(image)</code></pre>
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</div>
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<h3 class="max-sm:text-xl text-2xl my-4 text-secondary font-bold">Step 4: Get the Best Prediction</h3>
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<p class="max-sm:text-lg text-xl">
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The model returns a list of possible digits with scores. We choose the one with the highest score.
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</p>
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<div class="mockup-code mt-2">
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<pre><code>best_prediction = max(result, key=lambda x: x["score"])
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best_label = best_prediction["label"]</code></pre>
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</div>
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<h2 class="max-sm:text-2xl text-3xl my-5 text-primary font-bold">Why This Model?</h2>
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<ul class="max-sm:text-lg text-xl list-disc list-inside">
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<li>✅ Trained on real-world digit images from the MNIST dataset.</li>
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<li>✅ Fast and lightweight — ideal for web apps and educational tools.</li>
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<li>✅ Easy to integrate using Hugging Face’s <code>pipeline</code> interface.</li>
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</ul>
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<h2 class="max-sm:text-2xl text-3xl my-5 text-primary font-bold">Try Drawing!</h2>
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<p class="max-sm:text-lg text-xl">
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Head over to the classifier page and draw a number to see the magic happen in real-time! 🎨
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</p>
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</div>
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{% endblock %}

‎Algolyzer/study/templates/study/home.html‎

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<div class="stat">
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<div class="stat-figure text-primary">
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<svg
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viewBox="0 0 24 24"
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stroke-linecap="round"
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d="M12 8v4l3 3m6-3a9 9 0 11-18 0 9 9 0 0118 0z"></path>
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xmlns="http://www.w3.org/2000/svg"
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fill="none"
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class="inline-block h-8 w-8 stroke-current">
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<path
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stroke-linecap="round"
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d="M12 8v4l3 3m6-3a9 9 0 11-18 0 9 9 0 0118 0z"></path>
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</svg>
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</div>
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<div class="stat-title">Avg Time To Read</div>
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<div class="stat-value text-primary">7 Mins</div>
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<div class="stat-desc"></div>
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<div class="stat-title">Avg Time To Read</div>
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<div class="stat-value text-primary">7 Mins</div>
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<div class="stat-desc"></div>
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<div class="stat">
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<div class="stat-title">Appx. Words</div>
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<div class="stat-value text-secondary">1,200</div>
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<div class="stat-desc"></div>
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<div class="stat-title">Appx. Words</div>
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<div class="stat-value text-secondary">1,200</div>
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<div class="stat-desc"></div>
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</div>
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</div>
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</a>
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{% comment %} Text Classifier {% endcomment %}
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<a href="#" class="card bg-base-200 shadow-xl lg:p-4 hover:bg-base-300 border-solid border-primary border-2 hover:border-secondary">
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{% comment %} Doodle Classifier {% endcomment %}
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<a href="{% url 'doodle_classifier_study' %}" class="card bg-base-200 shadow-xl lg:p-4 hover:bg-base-300 border-solid border-primary border-2 hover:border-secondary">
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<div class="card-body">
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<h3 class="text-3xl font-semibold capitalize">Doodle Classifier</h3>
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<div class="stats stats-vertical lg:stats-horizontal shadow">
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<div class="stat">
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<div class="stat-figure text-primary">
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<svg
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xmlns="http://www.w3.org/2000/svg"
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fill="none"
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viewBox="0 0 24 24"
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class="inline-block h-8 w-8 stroke-current">
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<path
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stroke-linecap="round"
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stroke-linejoin="round"
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stroke-width="2"
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d="M12 8v4l3 3m6-3a9 9 0 11-18 0 9 9 0 0118 0z"></path>
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</svg>
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</div>
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<div class="stat-title">Avg Time To Read</div>
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<div class="stat-value text-primary">4 Mins</div>
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<div class="stat-desc"></div>
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</div>
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<div class="stat">
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<div class="stat-title">Appx. Words</div>
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<div class="stat-value text-secondary">550</div>
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<div class="stat-desc"></div>
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</div>
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</div>
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</div>
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</a>
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{% comment %} Linear Regression {% endcomment %}
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<a href="{% url 'linear_regression_study' %}" class="card bg-base-200 shadow-xl lg:p-4 hover:bg-base-300 border-solid border-primary border-2 hover:border-secondary">
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<div class="card-body">
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<h3 class="text-3xl font-semibold capitalize">Linear Regression</h3>
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<div class="stats stats-vertical lg:stats-horizontal shadow">
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<div class="stat">
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<div class="stat-figure text-primary">
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<svg
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xmlns="http://www.w3.org/2000/svg"
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fill="none"
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viewBox="0 0 24 24"
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class="inline-block h-8 w-8 stroke-current">
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<path
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stroke-linecap="round"
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stroke-linejoin="round"
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stroke-width="2"
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d="M12 8v4l3 3m6-3a9 9 0 11-18 0 9 9 0 0118 0z"></path>
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</svg>
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</div>
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<div class="stat-title">Avg Time To Read</div>
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<div class="stat-value text-primary">3 Mins</div>
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<div class="stat-desc"></div>
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</div>
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<div class="stat">
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<div class="stat-title">Appx. Words</div>
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<div class="stat-value text-secondary">500</div>
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<div class="stat-desc"></div>
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</div>
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</div>
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</div>
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</a>
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{% comment %} K-Means Clustering {% endcomment %}
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<a href="{% url 'k_means_clustering' %}" class="card bg-base-200 shadow-xl lg:p-4 hover:bg-base-300 border-solid border-primary border-2 hover:border-secondary">
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<div class="card-body">
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<h3 class="text-3xl font-semibold capitalize">Text Classifier</h3>
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<h3 class="text-3xl font-semibold capitalize">K-Means Clustering</h3>
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<div class="stats stats-vertical lg:stats-horizontal shadow">
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<div class="stat">
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<div class="stat-figure text-primary">
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<svg
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xmlns="http://www.w3.org/2000/svg"
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fill="none"
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viewBox="0 0 24 24"
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class="inline-block h-8 w-8 stroke-current">
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<path
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stroke-linecap="round"
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stroke-linejoin="round"
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stroke-width="2"
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d="M12 8v4l3 3m6-3a9 9 0 11-18 0 9 9 0 0118 0z"></path>
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xmlns="http://www.w3.org/2000/svg"
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fill="none"
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viewBox="0 0 24 24"
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class="inline-block h-8 w-8 stroke-current">
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<path
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stroke-linecap="round"
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stroke-linejoin="round"
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stroke-width="2"
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d="M12 8v4l3 3m6-3a9 9 0 11-18 0 9 9 0 0118 0z"></path>
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</svg>
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</div>
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<div class="stat-title">Avg Time To Read</div>
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<div class="stat-value text-primary">3 Mins</div>
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<div class="stat-desc"></div>
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<div class="stat-title">Avg Time To Read</div>
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<div class="stat-value text-primary">4 Mins</div>
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<div class="stat-desc"></div>
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</div>
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<div class="stat">
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<div class="stat-title">Appx. Words</div>
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<div class="stat-value text-secondary">500</div>
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<div class="stat-desc"></div>
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<div class="stat-title">Appx. Words</div>
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<div class="stat-value text-secondary">550</div>
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<div class="stat-desc"></div>
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</div>
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</div>
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{% comment %} Image Recognition {% endcomment %}
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<a href="#" class="card bg-base-200 shadow-xl lg:p-4 hover:bg-base-300 border-solid border-primary border-2 hover:border-secondary">
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{% comment %} SVM Regression {% endcomment %}
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<a href="{% url 'svm_regression' %}" class="card bg-base-200 shadow-xl lg:p-4 hover:bg-base-300 border-solid border-primary border-2 hover:border-secondary">
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<div class="card-body">
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<h3 class="text-3xl font-semibold capitalize">Image Recognition</h3>
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<h3 class="text-3xl font-semibold capitalize">SVM Regression</h3>
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d="M12 8v4l3 3m6-3a9 9 0 11-18 0 9 9 0 0118 0z"></path>
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xmlns="http://www.w3.org/2000/svg"
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fill="none"
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viewBox="0 0 24 24"
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class="inline-block h-8 w-8 stroke-current">
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<path
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stroke-linecap="round"
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d="M12 8v4l3 3m6-3a9 9 0 11-18 0 9 9 0 0118 0z"></path>
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</svg>
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<div class="stat-title">Avg Time To Read</div>
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<div class="stat-value text-primary">15 Mins</div>
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<div class="stat-desc"></div>
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<div class="stat-title">Avg Time To Read</div>
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<div class="stat-value text-primary">4 Mins</div>
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<div class="stat-title">Appx. Words</div>
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<div class="stat-title">Appx. Words</div>
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<div class="stat-value text-secondary">600</div>
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