This code implements a Digit Detection system using a Deep Neural Network (DNN). It's designed to recognize hand-written digits from images. The code is written in Python and utilizes essential deep-learning libraries. This project showcases your proficiency in computer vision and machine learning.
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Data Handling The code reads a dataset of hand-written digits from a CSV file using the pandas library. The dataset is split into a development set (X_dev and Y_dev) and a training set (X_train and Y_train). Pixel values are normalized to the range [0, 1] for consistent processing.
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Neural Network Setup: Parameters for the neural network are initialized, including weights and biases for two layers. Activation functions (ReLU and softmax) and their derivatives are defined. Forward propagation is implemented to compute predictions of the network. Backward propagation is used to calculate gradients for optimization.
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Training: The neural network parameters are updated using gradient descent. Training is repeated for a specified number of iterations, allowing the model to learn and improve. The accuracy of the model on the development set is printed during training.
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Prediction: A function is implemented to make predictions on new data. The code can be used to recognize digits in real-world scenarios.
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Visualization: Example predictions can be visualized to demonstrate the effectiveness of the model.
This code can be used for various applications, such as automated digit recognition in forms, checks, or handwritten notes. It serves as a foundation for more complex tasks in computer vision and deep learning.
Python programming. Understanding of basic deep learning concepts, including neural networks, activation functions, and gradient descent. Data preprocessing and handling using pandas. Implementation of forward and backward propagation. Model training and evaluation. Real-world application of machine learning for digit recognition.