Max Pooling in Convolutional Neural Network

CNN in deep learning, in this course we will learn about CNN in deep learning course. Convolutional Neural Networks (CNNs) are a powerful type of neural network used primarily for image recognition, object detection, and visual data analysis. This course introduces you to the foundational concepts of CNNs including convolutional layers, pooling, activation functions, and fully connected layers. You’ll explore how CNNs work with image data and how to implement them using Python and popular libraries like TensorFlow and Keras. We’ll cover practical applications such as image classification, face recognition, and medical image processing. Additionally, the course dives into advanced architectures like VGG, ResNet, and Inception to demonstrate real-world use cases. Through hands-on projects, you will build and train CNN models, evaluate performance, and apply data augmentation techniques. By the end of this course, you will have the skills to design and deploy CNNs for a variety of computer vision tasks with confidence. Learn With Jay