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Showing posts from December 11, 2022

Batch Normalization (BN) and Its Effects on the Learning Process of Convolutional Neural Networks

  Training a neural network can sometimes be difficult due to various reasons. Some of these reasons include: • Insufficient data: Training a neural network requires a large amount of data, and if the dataset is small or lacks diversity, the network may not be able to learn effectively. • Poor data quality: The quality of the training data can also impact the network's ability to learn. If the data is noisy, contains errors, or is not representative of the real-world data the network will be used on, the network may not be able to learn effectively. • Overfitting: Overfitting occurs when a neural network learns the patterns in the training data too well and is not able to generalize to new, unseen data. This can make the network perform poorly on real-world data. • Local minima: Neural networks can get stuck in local minima, which are suboptimal solutions to the training problem. This can make it difficult for the network to find the global optimum solution. • Vanishing o

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