Activation Functions in Deep Learning Sigmoid Tanh and Relu Activation Function

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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

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