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    <title>FiqLab</title>
    <description>FiqLab is the personal academic website of Taufiqurrahman featuring research publications, AI projects, and blog articles on Computer Vision, Deep Learning, YOLO, and IoT systems.</description>
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      <title><![CDATA[YOLO vs SSD vs Faster R-CNN: A 2026 Performance Comparison]]></title>
      <description><![CDATA[Comprehensive benchmark comparison of YOLO, SSD, and Faster R-CNN on COCO dataset — including mAP, FPS, parameters, and practical recommendations for real-time object detection in 2026.]]></description>
      <link>https://fiqlab.vercel.app/blog/yolo-vs-ssd-vs-faster-rcnn-comparison</link>
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      <pubDate>Sat, 18 Apr 2026 00:00:00 GMT</pubDate>
      <content:encoded><![CDATA[Comprehensive benchmark comparison of YOLO, SSD, and Faster R-CNN on COCO dataset — including mAP, FPS, parameters, and practical recommendations for real-time object detection in 2026.]]></content:encoded>
      <category>YOLO</category><category>SSD</category><category>Faster R-CNN</category><category>Object Detection</category><category>Deep Learning</category><category>Benchmark</category>
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      <title><![CDATA[Bitcoin Price Prediction Using ARIMA: A 2026 Update]]></title>
      <description><![CDATA[ARIMA is not dead. In 2026, it still offers a strong baseline for Bitcoin forecasting — if you use it correctly, validate it honestly, and understand where it fails.]]></description>
      <link>https://fiqlab.vercel.app/blog/bitcoin-price-prediction-using-arima</link>
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      <pubDate>Tue, 14 Apr 2026 00:00:00 GMT</pubDate>
      <content:encoded><![CDATA[ARIMA is not dead. In 2026, it still offers a strong baseline for Bitcoin forecasting — if you use it correctly, validate it honestly, and understand where it fails.]]></content:encoded>
      <category>Bitcoin</category><category>ARIMA</category><category>Time Series</category><category>Forecasting</category><category>Python</category>
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      <title><![CDATA[K-Nearest Neighbors: A Complete Manual Calculation from Scratch]]></title>
      <description><![CDATA[KNN looks simple on the surface — find the nearest neighbors, take a vote. But what actually happens mathematically? This article computes every single step: Euclidean distance, Manhattan distance, normalization, classification, and regression — digit by digit.]]></description>
      <link>https://fiqlab.vercel.app/blog/knn-manual-calculation</link>
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      <pubDate>Sat, 11 Apr 2026 00:00:00 GMT</pubDate>
      <content:encoded><![CDATA[KNN looks simple on the surface — find the nearest neighbors, take a vote. But what actually happens mathematically? This article computes every single step: Euclidean distance, Manhattan distance, normalization, classification, and regression — digit by digit.]]></content:encoded>
      <category>Machine Learning</category><category>KNN</category><category>Math</category><category>Tutorial</category><category>Algorithm</category>
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      <title><![CDATA[The Mac Mini M4 Is the Best AI Server You're Not Running Yet]]></title>
      <description><![CDATA[Everyone's obsessing over GPU clusters and cloud spend. Meanwhile, a $599 box sitting on my desk is running a 14B parameter LLM, 24/7, for roughly $52 a year in electricity. Let's talk about why Apple Silicon quietly won the local AI inference race.]]></description>
      <link>https://fiqlab.vercel.app/blog/mac-mini-m4-ai-server</link>
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      <pubDate>Sat, 11 Apr 2026 00:00:00 GMT</pubDate>
      <content:encoded><![CDATA[Everyone's obsessing over GPU clusters and cloud spend. Meanwhile, a $599 box sitting on my desk is running a 14B parameter LLM, 24/7, for roughly $52 a year in electricity. Let's talk about why Apple Silicon quietly won the local AI inference race.]]></content:encoded>
      <category>AI</category><category>Apple Silicon</category><category>Local LLM</category><category>Hardware</category><category>Opinion</category>
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      <title><![CDATA[Backpropagation: A Complete Step-by-Step Manual Calculation]]></title>
      <description><![CDATA[The most detailed guide to understanding backpropagation — from forward pass and loss computation to weight updates, calculated digit by digit with nothing hidden.]]></description>
      <link>https://fiqlab.vercel.app/blog/backpropagation-manual-calculation</link>
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      <pubDate>Wed, 08 Jan 2025 00:00:00 GMT</pubDate>
      <content:encoded><![CDATA[The most detailed guide to understanding backpropagation — from forward pass and loss computation to weight updates, calculated digit by digit with nothing hidden.]]></content:encoded>
      <category>Deep Learning</category><category>Neural Networks</category><category>Math</category><category>Tutorial</category>
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      <title><![CDATA[Getting Started with YOLOv8: Real-Time Object Detection for Everyone]]></title>
      <description><![CDATA[A comprehensive guide to YOLOv8 — the state-of-the-art object detection framework from Ultralytics. Learn how to train, optimize, and deploy your first model.]]></description>
      <link>https://fiqlab.vercel.app/blog/introduction-to-yolov8</link>
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      <pubDate>Fri, 15 Nov 2024 00:00:00 GMT</pubDate>
      <content:encoded><![CDATA[A comprehensive guide to YOLOv8 — the state-of-the-art object detection framework from Ultralytics. Learn how to train, optimize, and deploy your first model.]]></content:encoded>
      <category>YOLO</category><category>Object Detection</category><category>Deep Learning</category><category>Tutorial</category>
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    <item>
      <title><![CDATA[Neural Network Architectures Explained with Diagrams]]></title>
      <description><![CDATA[A visual walkthrough of common deep learning architectures — from feedforward networks to CNNs, RNNs, and Transformers — using interactive diagrams.]]></description>
      <link>https://fiqlab.vercel.app/blog/neural-network-architectures-explained</link>
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      <pubDate>Fri, 15 Nov 2024 00:00:00 GMT</pubDate>
      <content:encoded><![CDATA[A visual walkthrough of common deep learning architectures — from feedforward networks to CNNs, RNNs, and Transformers — using interactive diagrams.]]></content:encoded>
      <category>Deep Learning</category><category>Neural Networks</category><category>Computer Vision</category><category>Explainer</category>
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      <title><![CDATA[Deploying Computer Vision Models on IoT Edge Devices: Lessons Learned]]></title>
      <description><![CDATA[Practical insights from deploying YOLO-based detection on Raspberry Pi, ESP32-CAM, and Jetson Nano — covering quantization, latency optimization, and MQTT integration.]]></description>
      <link>https://fiqlab.vercel.app/blog/computer-vision-iot-edge</link>
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      <pubDate>Fri, 20 Sep 2024 00:00:00 GMT</pubDate>
      <content:encoded><![CDATA[Practical insights from deploying YOLO-based detection on Raspberry Pi, ESP32-CAM, and Jetson Nano — covering quantization, latency optimization, and MQTT integration.]]></content:encoded>
      <category>IoT</category><category>Edge AI</category><category>Raspberry Pi</category><category>YOLO</category><category>Embedded</category>
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    <item>
      <title><![CDATA[Detecting Plant Diseases with CNN: From Lab to Field]]></title>
      <description><![CDATA[How we built a plant disease detection system using convolutional neural networks that achieves 96.7% accuracy and runs offline on a $15 ESP32-S3 module.]]></description>
      <link>https://fiqlab.vercel.app/blog/deep-learning-plant-disease</link>
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      <pubDate>Mon, 08 Jul 2024 00:00:00 GMT</pubDate>
      <content:encoded><![CDATA[How we built a plant disease detection system using convolutional neural networks that achieves 96.7% accuracy and runs offline on a $15 ESP32-S3 module.]]></content:encoded>
      <category>CNN</category><category>Plant Disease</category><category>Precision Agriculture</category><category>TFLite</category><category>IoT</category>
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