100 Days of Deep Learning - Activation Functions in Deep Learning | Sigmoid, Tanh and Relu Activation Function

In artificial neural networks, each neuron forms a weighted sum of its inputs and passes the resulting scalar value through a function referred to as an activation function or transfer function. In this video, we explain the basics of Sigmoid, Tanh, and Relu—important parts of how computers learn. Notes: https://learnwith.campusx.in/s/store/courses/YouTube%20Notes 👍If you find this video helpful, consider giving it a thumbs up and subscribing for more educational videos on data science! 💭Share your thoughts, experiences, or questions in the comments below. I love hearing from you! ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 ✨ Hashtags✨ #SimpleLearning #ActivationFunctionsExplained #EasyTech ⌚Time Stamps⌚ 00:00 - Intro 00:47 - What are activation functions? 03:28 - Importance of AF 04:58 - Code Demo 06:38 - Why activation functions are needed? 11:05 - Ideal Activation function 18:41 - Sigmoid Activation Function 20:37 - Advantages 22:56 - Disadvantages 36:15 - Tan h Activation Function 38:00 - Advantages 39:02 - Disadvantages 40:17 - Relu Activation Function 40:50 - Advantages 42:43 - Disadvantages 44:24 - Outro