Machine Learning with Python

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Machine Learning Specialization — Study Notes & Labs Personal notes and lab notebooks from the Machine Learning Specialization by DeepLearning.AI & Stanford Online (Coursera), instructed by Prof. Andrew Ng. 📂 Repo: https://github.com/TruongDat05/machine-learning-notes-and-code

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🔥 Convolutional Neural Networks: Clearly explained! 🖼 Convolutional Neural Networks (CNNs): CNNs belong to the deep learning methods with layers like convolutional, pooling, and fully-connected layers that transform input images for recognition. ➡️ Feedforward Process: Data flows from input to output layers. Images undergo convolution operations, ReLu activation, and Max-Pooling to reduce size and enhance translation and scaling invariance. Finally, data is classified through a fully connected network. 🔄 Training Process: The training involves batches, backpropagation, and gradient descent to minimize errors. The weights start with random values and are updated through backpropagation. This cycle repeats until accuracy is achieved. 📊 Use Cases: CNNs excel in processing images, videos, and audio for tasks like classification, segmentation, and object detection. ⚠️ Limitations: While CNNs handle translation and scaling well, they struggle with rotation invariance. Want to learn more about CNNs? Then, check out super-detailed article about it. 👇 https://lnkd.in/eyA_DnYj

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