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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. Digital Notes for Deep Learning: https://shorturl.at/NGtXg 👍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:   / campusx-official   CampusX on Instagram for daily tips:   / campusx.official   My LinkedIn:   / nitish-singh-03412789   Discord:   / discord   ✨ 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