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
Why do we shuffle the "buys" and "sells" lists after balancing them in the context of building a recurrent neural network for predicting cryptocurrency price movements?
Sunday, 13 August 2023
by EITCA Academy
Shuffling the "buys" and "sells" lists after balancing them is a crucial step in building a recurrent neural network (RNN) for predicting cryptocurrency price movements. This process helps to ensure that the network learns to make accurate predictions by avoiding any biases or patterns that may exist in the sequential data. When training an RNN,