Deep Learning | Video 3 | Part 3 | CNN Architecture | Venkat Reddy AI Classes

Course Materials https://github.com/venkatareddykonasani/Youtube_videos_Material To keep up with the latest updates, join our WhatsApp community: https://chat.whatsapp.com/GidY7xFaFtkJg5OqN2X52k In this video, we delve into the fundamentals of Convolutional Neural Networks (CNN) architecture and its components step-by-step. Starting with the input layer and proceeding through convolution layers, pooling layers, and more, we demystify how CNNs detect features and perform image classification. We explore the importance of: Convolution layers for detecting features Pooling layers to reduce image size and complexity Flattening for connecting to fully connected layers Activation functions like ReLU for nonlinearities Optimizers such as Adam for training efficiency Understand the significance of filter sizes, kernel sizes, and activation functions like ReLU for effective feature detection. Follow along as we code a basic CNN model using TensorFlow/Keras, implementing layers for low-level, mid-level, and high-level feature detection. Learn how to preprocess image data, load datasets, and build CNN models for image classification tasks. Gain insights into parameter choices, layer configurations, and the role of convolution and pooling in feature extraction. Join us in this deep dive into CNN architecture with practical examples, empowering you to grasp the essentials of building and training convolutional networks for image analysis and classification tasks. #CNN #ConvolutionalNeuralNetworks #DeepLearning #ai #NeuralNetworks #ImageClassification #MachineLearning #DataScience #genai #promptengineering