Category Archives: Deep Learning
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:
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…
What is quantum machine learning?
Quantum machine learning is an interdisciplinary field that combines principles from quantum physics and machine learning. It involves using quantum algorithms to process and analyze data for tasks such as classification and regression. One example of quantum machine learning is using a quantum computer to perform supervised learning, which involves training a model to make…
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…