Category Archives: Python
Missing data imputation for deep learning tasks
Let’s say you have a dataset with missing values that you want to use to train a deep neural network. You can use the following approach to handle the missing values: Here is some sample code that demonstrates how to handle missing values in a deep neural network using this approach:
NumPy’s reshape
NumPy is a powerful library for working with multi-dimensional arrays in Python. One useful function it provides is numpy.reshape, which allows you to reshape an array to a new shape. Here is an example of using numpy.reshape to reshape a 1-dimensional array of integers into a 3×3 2-dimensional array: The output will be: You can…
Autoencoders
An autoencoder is a type of neural network used to learn an input’s compressed representation. It is composed of two parts: an encoder and a decoder. The encoder takes in an input and converts it into a hidden representation, or encoding, which is typically smaller in size than the input. The decoder then converts this…
How to create a Convolutional Neural Network in Python?
Convolutional neural networks (CNNs) are a type of neural network that is particularly well-suited for image classification tasks. Here is an example of how you might create a simple CNN in Python using the popular deep learning library Keras: This code creates a simple CNN with two convolutional layers and two fully connected layers. The…