You have a dataset of 1,000,000 labeled 200x200 images indicating whether a car is present or not. Which neural network architecture and output layer choice is most likely to produce the highest accuracy for detecting the presence of a car in an image?

A deep Convolutional Neural Network (CNN) is ideal for image classification tasks because it can automatically learn hierarchical features from raw pixel data, such as edges, textures, and object parts, which are crucial for detecting cars. Multilayer Perceptrons (MLPs) treat image pixels as independent features, losing spatial relationships, making them less effective for image data. For a binary classification problem (car present or not), a softmax output layer is appropriate. While it's typically used for multi-class classification, for binary cases, it outputs two probabilities that sum to one, effectively representing the probability of each class. A linear output layer is generally used for regression tasks, predicting continuous values, not probabilities for classification.