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neural-network-architecture

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This research presents a hybrid deep learning framework combining MobileNet V2 with LSTM, GRU, and Bidirectional LSTM for classifying various potato diseases. The study explores the performance of different architectures to determine the optimal configuration for accurate disease categorization.

  • Updated Aug 10, 2024
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This project demonstrates how to build an image classification model using Convolutional Neural Networks (CNNs) to classify images into predefined categories. It covers data preprocessing, model building, training, and evaluation steps.

  • Updated Feb 13, 2025
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This repository implements a 3-layer neural network with L2 and Dropout regularization using Python and NumPy. It focuses on reducing overfitting and improving generalization. The project includes forward/backward propagation, cost functions, and decision boundary visualization. Inspired by the Deep Learning Specialization from deeplearning.ai.

  • Updated Feb 19, 2025
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Lecture notes, resources and programming assignments taken from the specialized deep learning program (a sequence of courses all related to deep learning) offered by DeepLearning.AI on coursera.

  • Updated Aug 13, 2024
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