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In this article, we’ll delve deeper into the mechanisms of machine learning. First, we’ll introduce the concept of deep learning (DL). Then, we’ll explore artificial neural networks (ANNs) in detail, examining how they operate and identifying key foundational ANNs from which others derive. In upcoming articles, we’ll provide practical code examples for building each of the afore mentioned key ANN to solve real-world problems.
Deep learning is a powerful subset of machine learning that has revolutionized the field of artificial intelligence by enabling computers to learn directly from large amounts of data. Unlike traditional machine learning algorithms that require explicit programming of features, deep learning models automatically discover relevant features from raw data through the use of artificial neural networks.
At the core of deep learning are artificial neural networks (ANNs). These networks are inspired by the structure and function of the human brain, with interconnected nodes, or neurons, that process and transmit information.
Each neuron receives input signals, processes them using some mathematical function, and applies an activation function to produce an output. Additionally, neurons typically include a bias term, which allows the model to account for input variables that may not be represented in the input data. Bias shifts the activation function, enabling the neuron to learn and make predictions even when all input values are zero.
ANNs consist of multiple layers, each containing interconnected neurons that perform specific functions. The input layer receives raw data, which is then passed through one or more hidden layers where complex computations occur. Finally, the output layer produces the model’s prediction or classification.
- Initialization: Initially, the connections between neurons, known as weights, are randomly assigned…