👉 Computing models refer to the algorithms and frameworks that enable machines to perform tasks typically requiring human intelligence, such as learning from data, understanding natural language, recognizing patterns, and making decisions. These models are broadly categorized into three types: supervised learning, where models learn from labeled data to predict outcomes; unsupervised learning, which identifies hidden structures in unlabeled data; and reinforcement learning, where models learn by interacting with an environment to maximize rewards. Deep learning, a subset of machine learning, utilizes neural networks with many layers (deep architectures) to model complex patterns in large datasets. These models are implemented using various computational frameworks, including TensorFlow and PyTorch, which provide tools for building, training, and deploying these sophisticated algorithms efficiently.
