ResNet Explained & Implemented in Python | CNN, Residual Blocks & Skip Connections | Deep Learning

In this video, I explain ResNet (Residual Neural Network) from the basics and implement its core concepts step by step using Python. Before understanding ResNet, I briefly cover Deep Learning and CNN (Convolutional Neural Network) to build the required foundation. Topics Covered What is Deep Learning? What is CNN? How CNN processes images Convolution and Feature Maps Batch Normalization ReLU Activation Function Residual Blocks Skip Connections in ResNet How ResNet works internally Implementing a simplified ResNet using Python Global Average Pooling Fully Connected Layer Softmax Final Class Prediction Understanding the complete ResNet pipeline ResNet Implementation Flow Input Image → Convolution → Batch Normalization → ReLU → Residual Blocks → Global Average Pooling → Fully Connected Layer → Softmax → Prediction I also explain how the individual Python functions work together to implement the different stages of a simplified ResNet architecture.