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We've all dealt with activation functions while working with neural nets. - Sigmoid - Tanh - ReLu & Leaky ReLu - Gelu Ever wondered why they are so important❓🤔 Let me explain it to you in this 🧵👇
Before we proceed I want you to understand something! You can think of a layer in neural net as a function & multiple layers makes the network a composite function. Now, a composite function consisting of individual linear functions is also linear. Check this 👇
We have a simple neural net that does binary classification. Scenario 1: - Linear decision boundary - Linear Activation function Observe how the neural net is able to quickly learn & loss converges to zero. Watch this 👇
Scenario 2: - Non Linear decision boundary - Linear Activation function Observe how the neural net struggles to learn & the loss consistently remains high! With linear activations it's unable to create a non-linear decision boundary. Watch this 👇
Scenario 3: - Non Linear decision boundary - Non-linear Activation function (Sigmoid) Observe how the neural net performs well this time. With a non-linear activation function we give the network ability to create a non-linear decision boundary. Watch this 👇
Now we understand why activation functions are important. Next time we see why do we need different flavours of these non-linear activation functions. What are the advantages of one over other. You can play around like i did in the videos here 👇 <a target="_blank" href="http://playground.tensorflow.org" color="blue">playground.tensorflow.org</a>
That's a wrap! If you interested in: - Python 🐍 - Data Science 📈 - Machine Learning 🤖 - Maths for ML 🧮 - MLOps 🛠 - NLP 🗣 - Computer Vision 🎥 - LLMs 🧠 I'm sharing daily content over here, follow me → @akshay_pachaar if you haven't already!! Cheers!! 🙂