Deep Learning Complete Course | Part 2| CNN implementation.

Instructor – Akarsh Vyas Welcome back! In this video, we’ll take the next big step in Deep Learning and dive deep into Convolutional Neural Networks (CNNs) the architecture that completely changed the world of Computer Vision. You can download the code and datasets from here: Code Link – https://github.com/AkarshVyas/CNN-video 📘 All the notes of our classes are here: Notes – https://drive.google.com/file/d/15b8U... Here’s what you’ll learn in this CNN deep dive: The problem with ANN on images and why CNN was invented The intuition behind Convolutions, Filters, and Feature Maps Pooling layers and why they make CNNs efficient Step-by-step architecture: Convolution → Pooling → Fully Connected Famous CNN models (LeNet, AlexNet, VGG, ResNet) and how they shaped modern AI Hands-on coding: Building CNNs with TensorFlow/Keras on real datasets Applications of CNNs in real life — from face recognition to self-driving cars These are the most important building blocks of modern Computer Vision. If you’ve understood ANNs from our first video, this session will complete the foundation you need before moving to advanced architectures and real-world AI projects. By the end of this video, you’ll not only understand how CNNs work but also be able to build and train your own CNN from scratch. 0:00:00 - 00:00:38 - introduction 00:00:38 - 00:11:53- CNN(introduction) 00:11:55 - 00:19:00 - How CNN works 00:19:00 - 00:21:34 - Understanding the Architecture 00:21:34 - 00:25:03 - Layers in CNN 00:25:03 - 00:42:12 - Edge Finding in CNN 00:42:12 - 00:48:52 - understanding(padding and strides) 00:48:52 - 00:55:26 - why we use strides 00:55:26 - 01:04:41- pooling 01:04:41 - 01:06:47- max pooling 01:06:47 - 01:15:20 - Revision and flattening 01:15:20 - 01:54:13 - code implementation 01:54:13 - 01:54:47 - outro