Perceptron Learning NAND Gate | Update Weights Bais ANN in Machine Learning by Vidya Mahesh Huddar скачать в хорошем качестве

Perceptron Learning NAND Gate | Update Weights Bais ANN in Machine Learning by Vidya Mahesh Huddar

Perceptron Learning NAND Gate | Update Weights Bais Artificial Neural Networks in Machine Learning by Vidya Mahesh Huddar In this video, we solve a complete numerical example of Perceptron Learning for NAND Gate in Artificial Neural Networks. ✔️ Derivation of Perceptron Learning Rule (Step Activation Function) ✔️ NAND Gate Truth Table ✔️ Weight Update Calculation (Epoch 1 & Epoch 2) ✔️ Final Weights and Bias Calculation ✔️ Training Convergence ✔️ Decision Boundary Equation ✔️ 2D Plot Explanation Initial Values: w1 = −0.1 w2 = −0.3 b = 0.5 Learning Rate (η) = 0.1 Training converges in 2 epochs with final values: w1 = −0.2 w2 = −0.4 b = 0.4 This video is very useful for: • Machine Learning students • Artificial Neural Network exams • ANN numerical problems • Perceptron learning algorithm understanding ******************************** Follow Us on: 1. Blog / Website: https://www.vtupulse.com/ 2. Download Final Year Project Source Code: https://vtupulse.com/download-final-y... 3. Like Facebook Page:   / vtupulse   4. Follow us on Instagram:   / vtupulse   5. Like, Share, Subscribe, and Don't forget to press the bell ICON for regular updates