Data is the foundation of all machine learning algorithms. In order for a machine learning model to learn and make accurate predictions, it needs to be trained on large amounts of relevant and diverse data. The quality and quantity of data used to train a model can greatly impact its performance and accuracy. It’s important …
Today’s lesson is about generative adversarial networks (GANs), a type of deep learning model that is used for generating new data that resembles a given training dataset. GANs consist of two main components: a generator and a discriminator. The generator takes random noise as input and tries to generate data that resembles the training data. …
Today, let’s talk about bias in machine learning. Bias in machine learning refers to the phenomenon where a model learns and perpetuates stereotypes or prejudices present in the training data. This can lead to unfair or discriminatory outcomes, such as certain groups being disproportionately impacted by the decisions made by the model. One of the …
