ResNet, short for Residual Neural Network, is a specific type of neural network used in deep learning for computer vision applications. It was introduced in 2015 by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun in their paper "Deep Residual Learning for Image Recognition". ResNet is designed to support up to thousands of convolutional layers, which allows for more complex tasks and improved accuracy in image classification and recognition.

The key feature of ResNet is the use of residual connections, which allow for the creation of very deep neural networks without the problem of vanishing gradients. In a ResNet, each layer learns residual functions with reference to the layer inputs, and the network has skip connections that perform identity mappings, merged with the layer.

ResNet architecture consists of several types of building blocks, including Basic Blocks, Bottleneck Blocks, and Pre-activation Blocks. Basic Blocks are the simplest building blocks studied in the original ResNet, consisting of two sequential 3x3 convolutional layers and a residual connection. Bottleneck Blocks consist of three sequential convolutional layers and a residual connection, with the first layer being a 1x1 convolution for dimension reduction, the second layer performing a 3x3 convolution, and the last layer being another 1x1 convolution for dimension restoration. Pre-activation Blocks are used in DenseNets and approach the goal of connecting each layer to every other layer by using identity mappings as skip connections, but merge the layer output with skip connections by concatenation, not addition.

ResNet can be implemented with different numbers of layers, such as ResNet-50, ResNet-101, and ResNet-152, which are all based on Bottleneck Blocks. ResNet can be used with Keras, and the ResNet-50 model can be implemented using the function tf.keras.applications.ResNet50.

In summary, ResNet is a specific type of neural network used in deep learning for computer vision applications. It uses residual connections to allow for the creation of very deep neural networks without the problem of vanishing gradients. ResNet architecture consists of several types of building blocks, including Basic Blocks, Bottleneck Blocks, and Pre-activation Blocks, and can be implemented with different numbers of layers.