Recurrent Neural Networks (RNNs) are a special kind of deep learning model. They are great at handling data that comes in a sequence, like time series data. Unlike regular neural networks, RNNs remember what came before to help with the current task. This makes them perfect for tasks like understanding language, recognizing speech, and predicting…
Backpropagation is a key algorithm in training deep learning models. It’s a supervised learning method that helps deep neural networks learn and get better over time. The algorithm adjusts the network’s connection weights to reduce loss. It does this by calculating the loss function’s gradient for each weight. Then, it updates the weight to minimize…
Generative Adversarial Networks (GANs) are a new way to create data in deep learning. They use special kinds of neural networks. The main idea is to find patterns in data and make new examples that look like the original. GANs have two parts: a generator and a discriminator. They work together to make fake data…
Machine learning (ML) is a fast-growing field in Artificial Intelligence (AI). It helps systems learn and get better from data, without needing to be programmed. At its heart are two main types of algorithms: supervised learning, which includes classification and regression, and unsupervised learning. Classification and regression are key supervised learning methods. The main difference…
In machine learning, supervised and unsupervised learning differ in how they’re trained and the data they use. Supervised learning uses labeled data to make predictions or classifications. Unsupervised learning finds hidden patterns in unlabeled data without guidance. Supervised learning needs labeled data to learn from. It knows the input and output variables. Unsupervised learning finds…
