The Softmax Activation Function | Deep Learning baiscs

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In this video, we'll explore:
Why Sigmoid is not ideal for the output layer in multiclass classification ❌.
Using the Softmax function in real-world examples:
Stock market decisions 📈: Buy, Sell, or Hold.
Handwritten digit recognition ✏️: Classifying digits from 0 to 9.
Why Sigmoid Falls Short in Multiclass Classification 🚫
The sigmoid function is great for binary classification but not so much for multiclass problems. Here's why:
Sigmoid outputs values between 0 and 1 but doesn't ensure the probabilities add up to 1.
This can lead to confusion when interpreting results, especially when deciding between multiple classes.
Enter Softmax 🌈✨
The Softmax function is perfect for multiclass classification! Here's why:
Outputs Probabilities: Ensures all output values add up to 1, making it easy to interpret them as probabilities.
Example: For our stock market scenario, Softmax will help decide the probability of buying, selling, or holding a stock. Similarly, for digit recognition, it provides a probability distribution over all 10 digits.
What We Expect from an Output Activation Function 🧐
Probability-like Values: Outputs should look like probabilities.
Sum to 1: The sum of the outputs should be 1, forming a valid probability distribution.
Visualizing Sigmoid and Softmax 📉📈
We'll show you a graphical representation of the sigmoid function for a 3-class classification problem and explain its limitations.
Deep Dive into Softmax 💡
We'll closely examine Softmax and its properties:
Non-linearity: Softmax is non-linear ✅.
Differentiability: Softmax is differentiable ✅.
Zero-centeredness: Softmax is not zero-centered ❌.
Computational Efficiency: Softmax isn't the most efficient computationally ❌.
Saturation: Softmax can saturate, which isn't ideal, but it's manageable ❌.
Join us for this detailed yet simple tutorial on Softmax and understand why Softmax is the go-to activation function for output layer in case of multiclass classification tasks. 📚
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[@jaikumarmeena4769]

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[@prosmartanalytics]

Glad you liked it 👍