Description: The training phase is a crucial stage in machine learning where a model learns from a training dataset. During this phase, the model adjusts its internal parameters to minimize prediction error. This process involves the use of algorithms that optimize the loss function, which measures the discrepancy between the model’s predictions and the actual values. In the context of Generative Adversarial Networks (GANs), for example, two networks are trained: a generator and a discriminator, which compete against each other to improve their capabilities. The training phase is facilitated through the implementation of backpropagation and optimization techniques, allowing developers to efficiently tune models. The training phase is not only fundamental in supervised learning, where labels are used in the data, but it is also essential in unsupervised learning and reinforcement learning approaches. In summary, the training phase is the heart of machine learning, where models gain the ability to generalize from specific examples, enabling them to make predictions on unseen data.
