Ever struggled with convolutions and convolutional neural networks (CNNs)? You're not alone! Most tutorials dive straight into complex math or confusing code, but in this video, I’m going to show you how to build your intuition first, then I promise we're gonna dive deep into every detail of convolutions and make you a pro. Especially after I show you how to solve a real interview problem about this topic. You'll also get FREE access to the interactive visualization I made specifically for this video. In just one video, you'll grasp: ✅ Intuition First: The key analogy that made CNNs click for me, visualized through clear animations and relatable examples. ✅ 7 Key Concepts: Master input channels, groups, output channels, kernel size, stride, padding, and dilation—everything you need for your machine learning journey! ✅ Hands-On Coding: Step-by-step walkthrough solving an actual CNN interview question from Deep-ML (#41), with clear Python implementation. Whether you're a university student, bootcamp learner, mid-career professional, or STEM graduate transitioning into machine learning and data science, this comprehensive guide will help you understand CNNs deeply and clearly. Links: 1. Deep-ML Practice Site: 2. CNN Explainer Visualization Tool: 3. My Interactive Visualization Tool: 📚 Chapters: 0:00 Hook & overview 00:42 Why CNNs matter 01:16 Building CNN intuition 02:46 The math of convolutions 03:19 The 7 key CNN concepts 03:51 Input channels 04:15 Groups (grouped convolutions) 05:04 Output channels 05:45 Kernel size 06:18 Padding 06:50 Stride 07:54 Dilation 08:48 Coding problem overview 09:12 Reading the problem 09:35 Coding solution 10:44 Summary 🔥 Hashtags: #cnn #convolutionalneuralnetworks #deeplearning #machinelearning #datascience #ai #codingtutorial #machinelearningtutorial #deepml #learnpython #tensorflow #pytorch #mlinterviewprep #mlinterviewquestions #neuralnetworks #aiintuition #convolution #datasciencetutorial #stemcareers #careerswitch #transitiontodatascience #datasciencebootcamp #mlstudents #datascienceeducation #machinelearningeducation