{"id":5427,"date":"2023-02-01T20:20:59","date_gmt":"2023-02-01T20:20:59","guid":{"rendered":"https:\/\/techzizou.com\/?page_id=5427"},"modified":"2023-02-01T21:01:33","modified_gmt":"2023-02-01T21:01:33","slug":"ai-ml-tutorials","status":"publish","type":"page","link":"https:\/\/techzizou.com\/ai-ml-tutorials\/","title":{"rendered":"AI \/ ML Tutorials"},"content":{"rendered":"\n
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AI & ML Tutorials <\/strong><\/h1>\n\n\n\n
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GO TO BLOGS<\/strong><\/a><\/div>\n<\/div>\n\n\n
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Artificial Intelligence & Machine Learning<\/h1>\n\n\n\n

AI<\/strong> is a bigger concept to create intelligent machines that can simulate human thinking capability and behavior, on the other hand, machine learning<\/strong> is an application or subset of AI that allows machines to learn from data without being programmed explicitly. <\/p>\n\n\n\n

Deep learning<\/strong> (also known as deep structured learning<\/strong>) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.<\/p>\n\n\n\n

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Artificial intelligence is a technology using which we can create intelligent systems that can simulate human intelligence.<\/strong><\/em><\/p>\n<\/blockquote>\n\n\n\n

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Machine learning is a subfield of artificial intelligence, which enables machines to learn from past data or experiences without being explicitly programmed.<\/strong><\/em><\/p>\n<\/blockquote>\n\n\n\n

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Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters, or faces.<\/strong><\/em><\/p>\n<\/blockquote>\n\n\n\n

On this site, you will find tutorials to get you started with the real-world applications of Computer Vision and Machine Learning. <\/p>\n\n\n\n

Image Classification vs Object Detection<\/h4>\n\n\n\n

In Image Classification, we have one label per image while for object detection, we can have single or multiple labels per image as demonstrated below.<\/p>\n\n\n

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There are 3 types of detectors for deep learning-based object detection in machine learning.<\/p>\n\n\n\n

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  1. RCNN (<\/strong>Region-Based Convolutional Neural Networks) <\/strong>and its variants.<\/li>\n\n\n\n
  2. YOLO<\/strong> (You Only Look Once)<\/li>\n\n\n\n
  3. SSD<\/strong> (Single Shot Detectors)<\/li>\n<\/ol>\n\n\n\n
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    DATASET LABELING\/ANNOTATION<\/strong><\/h2>\n\n\n\n

    Get started with the process of training a Machine Learning model<\/p>\n\n\n\n

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    TRAINING YOLO MODEL<\/strong><\/h1>\n\n\n\n

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    USING GOOGLE COLAB<\/strong><\/h4>\n\n\n\n
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    Custom Object Detection<\/h2>\n\n\n\n

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    Train a YOLOv4 custom object detector<\/h3>\n\n\n\n
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    YOLOv4 vs YOLOv4-tiny (Custom object detectors)<\/h3>\n\n\n\n
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    Train a YOLOv4-tiny custom object detector<\/h3>\n\n\n\n
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    USING WINDOWS\/LINUX<\/strong><\/h4>\n\n\n\n
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    Custom Object Detection<\/span><\/h2>\n\n\n\n

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    WINDOWS<\/strong><\/h3>\n<\/div>\n\n\n\n
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    LINUX<\/strong><\/h3>\n<\/div>\n<\/div>\n\n\n\n
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    Install and run YOLOv4-Darknet on Windows<\/h3>\n\n\n\n
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    Train a custom YOLOv4 object detector on Windows<\/h3>\n\n\n\n
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    Read More<\/strong><\/a><\/div>\n<\/div>\n\n\n\n
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    Train a custom YOLOv4-tiny object detector on Windows <\/h3>\n\n\n\n
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    Install and run YOLOv4-Darknet on Linux<\/h3>\n\n\n\n
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    Train a custom YOLOv4 object detector on Linux <\/h3>\n\n\n\n
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    Read More<\/strong><\/a><\/div>\n<\/div>\n\n\n\n
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    Train a custom YOLOv4-tiny object detector on Linux <\/h3>\n\n\n\n
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    TRAINING DEEP LEARNING MODELS<\/strong><\/h1>\n<\/div><\/div>\n\n\n\n
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    For Object Detection <\/span><\/h2>\n\n\n\n
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    Train an SSD Model for object detection using Google Colab (TensorFlow 1.x)<\/h3>\n\n\n\n
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    Train an SSD Model for object detection using Google Colab (TensorFlow 2.x)<\/h3>\n\n\n\n
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    For Image Classification<\/span><\/h2>\n\n\n\n
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    Train an SSD Model for image classification using Google Colab (TensorFlow 2.x)<\/h3>\n\n\n\n
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    Train a model for custom Image Classification using Teachable-Machine and test it online<\/h3>\n\n\n\n
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    MOBILE ML<\/strong><\/h1>\n<\/div><\/div>\n\n\n\n

    In Mobile ML or Mobile machine learning the Model training still generally happens on server-side ML frameworks (like TensorFlow, PyTorch, etc), while model inference takes place on-device.<\/p>\n\n\n\n

    Some examples of machine learning on mobile devices are as follows:<\/p>\n\n\n\n