Implementing Dropout Regularization for Neural Networks in Deep Learning

Posted on November 18, 2022 by Shubham Dubey | Comments(1)

Artificial neural networks with numerous layers separating the inputs and outputs are what deep neural networks (deep learning) are (prediction). The likelihood of overfitting increases when the training dataset has a small number of examples. Overfitting occurs when the network can correctly predict training data samples but performs poorly and cannot generalize effectively on validation and test data.

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Spark DataFrames for Machine Learning Applications – Part 2

Posted on November 9, 2022 by Mohmmad Shahnawaz Ahangar | Comments(0)

Spark DataFrame is a collection of data organized in the form of columns and rows, where each column can be feature data. They are akin to database tables or Pandas DataFrames but with richer optimization, to integrate with large-scale datasets for machine learning applications or algorithms. A DataFrame fundamentally is a Dataset that is organized into named columns.

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