Why is it necessary to balance an imbalanced dataset when training a neural network in deep learning?
Sunday, 13 August 2023
by EITCA Academy
Balancing an imbalanced dataset is necessary when training a neural network in deep learning to ensure fair and accurate model performance. In many real-world scenarios, datasets tend to have imbalances, where the distribution of classes is not uniform. This imbalance can lead to biased and ineffective models that perform poorly on minority classes. Therefore, it
- Published in Artificial Intelligence, EITC/AI/DLPP Deep Learning with Python and PyTorch, Data, Datasets, Examination review
Tagged under:
ADASYN, Artificial Intelligence, Class Imbalance, Dataset Balancing, Deep Learning, Neural Networks, Oversampling, SMOTE, Undersampling
What are the steps involved in manually balancing the data in the context of building a recurrent neural network for predicting cryptocurrency price movements?
Sunday, 13 August 2023
by EITCA Academy
In the context of building a recurrent neural network (RNN) for predicting cryptocurrency price movements, manually balancing the data is a crucial step to ensure the model's performance and accuracy. Balancing the data involves addressing the issue of class imbalance, which occurs when the dataset contains a significant difference in the number of instances between