All posts by chicodelarosa
Reinforcement learning and how it works
Reinforcement learning is a type of machine learning that involves training an agent to make a series of decisions in an environment to maximize a reward. The agent learns through trial and error by taking actions and receiving feedback through rewards or penalties. Here’s how it works: Over time, the agent learns to take actions…
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…
Limitations of supervised learning
Supervised learning is a machine learning technique in which a model is trained on labeled data, meaning that the data consists of input features and the corresponding correct output labels. The goal of supervised learning is to make predictions on new, unseen data based on the patterns learned from the training data. While supervised learning…
Missing values and supervised learning
Missing values in supervised learning can be a major issue, as they can reduce the accuracy of the model and lead to poor performance. Several approaches can be taken to address missing values in supervised learning, including: Ultimately, the best approach to dealing with missing values in supervised learning will depend on the dataset’s specific…
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…