Deep Learning Explained
Deep learning, a subset of machine learning, is a powerful method for recognizing complex patterns in data using artificial neural networks (ANNs).
Deep learning, a subset of machine learning, is a powerful method for recognizing complex patterns in data. Deep learning uses artificial neural networks (ANNs), which are designed to simulate the way our brains work, to learn from vast amounts of data. Each node in a neural network takes in some input, does some computation with it, and then passes the result onto the next nodes. The network is "deep" because it has many layers of these nodes between the input and the output. Each layer learns to recognize different features of the data.
For example, if a deep learning system is trained to recognize images of cats, the first layer might learn to recognize simple shapes and colors. The next layer might learn to recognize more complex features like fur patterns, and the final layer might put it all together to recognize a cat. The key to deep learning is that it can automatically learn to recognize these features, without being explicitly programmed to do so. This is done through a process called training. During training, the system is shown lots of examples (like pictures of cats), and it gradually adjusts the connections between its nodes to get better at recognizing what it's looking for.
Two common types of deep learning architectures are Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). CNNs are primarily used for image processing tasks, as they can effectively learn spatial hierarchies of features. RNNs, on the other hand, are used for sequential data like time series or natural language, as they can maintain a form of 'memory' of past inputs in their hidden state.