Neural Networks Simplified | Core Concepts You MUST Know - Tamil
Learn Neural Networks from absolute basics in this clear and beginner-friendly Tamil explanation! In this video, we cover: 00:00 Intro 00:38 Our Nervous System 02:39 Forward Propagation 04:17 Forward Propagation With Example 14:10 Don't get scared about Maths 15:07 Back Propagation Explained 17:07 Loss Function with Example 19:03 Gradient Descent to Adjust Weights ✅ What is a Neural Network? ✅ Input Layer → Hidden Layer → Output Layer (full intuition) ✅ Forward Propagation Explained with Real Values ✅ Activation Functions (ReLU, Sigmoid) — When & Why ✅ Binary Cross Entropy Loss (simple explanation, no formulas needed!) ✅ How does Back propagation actually work? ✅ How Gradient Descent updates weights ✅ Real example: Fitness Prediction (Exercise, Junk Food, Sleep, Water) ✅ How weights become positive/negative based on learning ✅ Why learning rate should be small (visual explanation) This video is designed for students, beginners, and anyone learning AI/ML for the first time. No prior math knowledge needed — everything is explained with intuition and real-world examples. If you are preparing for interviews or building your ML fundamentals, this video is a must-watch. 👉 Like, Share, and Comment your questions! 👉 Subscribe for more AI, ML, Deep Learning, and GCP content in Tamil!

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