Deep Learning | Video 2 | Part 3 | Activation Functions in Neural Networks | 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 detailed tutorial, we delve into activation functions used in neural networks, their impact on model performance, and how to choose the right one for your task. We cover key concepts like sigmoid and tanh functions, exploring their differences and practical implications. Chapters: Activation Functions Explained Learn the basics of activation functions—essential components of neural networks that introduce non-linearity. We discuss popular functions like sigmoid and linear activation. Sigmoid vs. Tanh Dive into the differences between sigmoid and tanh functions. Discover how tanh, with its wider range, can sometimes lead to faster convergence in certain scenarios. Practical Demo: Comparing Sigmoid and Tanh We walk through a practical demonstration comparing the execution times and convergence rates of sigmoid and tanh activation functions. Vanishing Gradient Problem Understand the challenge of vanishing gradients in deep neural networks, particularly caused by activation functions like sigmoid. Explore how this issue affects learning and model performance. Introducing ReLU (Rectified Linear Unit) Discover the rectified linear unit (ReLU), a popular activation function designed to combat the vanishing gradient problem by maintaining a non-zero gradient. #NeuralNetworks #ActivationFunctions #DeepLearning #MachineLearning #VanishingGradients #Sigmoid #Tanh #ReLU #AIAlgorithms #promptengineering