Deep learning, a subset of machine learning, is gaining widespread attention due to its ability to surpass human-level performance in tasks such as image recognition. It primarily employs neural network architectures, where the term "deep" refers to multiple hidden layers in these networks. Artificial Neural Networks (ANNs) consist of an input layer, hidden layers, and an output layer, with neurons connected to perform computations. Key components like weights, biases, activation functions, and methods such as backpropagation and gradient descent optimize the network's performance by reducing errors. Deep learning models are praised for eliminating the need for manual feature engineering and are highly effective in complex tasks like image classification and natural language processing. However, they require significant computational resources, large amounts of labeled data, and often lack interpretability, posing challenges despite their accuracy. Understanding the underlying math and functions is crucial for deciding when to use deep learning, considering its high cost and training time.