Regularization in Deep Learning: Intuition Behind

Regularization 101

💡 Regularization in Deep Learning: Intuition Behind

Regularization 101 definition:
A model does well on training data but not so well on unseen data. Overfitting :)

But is there more to that? Let’s figure it out.

Remember that one guy in school who memorized everything written in books or uttered by the teacher but didn’t perform well when questions were twisted a bit.

What happened?

He only memorized the lessons but didn’t understand the concepts behind them to apply to previously unseen questions.

That’s overfitting, and to correct that, we need regularization.

Regularization acts like a good teacher, guiding the student to focus on core concepts rather than memorizing irrelevant details.

Regularization essentially solves 3 problems:

1️⃣ Overfitting: Prevents the model from fitting the noise or irrelevant details in the training data.

2️⃣ Model Complexity: Reduces the complexity of the model by constraining its capacity, ensuring it doesn’t overlearn.

3️⃣ Bias-Variance Tradeoff: Strikes a balance between underfitting (too simple) and overfitting (too complex).

So, how do we do regularization?
Quite a few ways, actually.

Let’s see the most important ones—and let’s try to understand them without any math, shall we?

1️⃣ L1 and L2 Regularization – A way to discourage large weights. A penalty term ensures large weights are dampened.

• L1: Penalty added to absolute weights.
• L2: Penalty added to squared weights.

2️⃣ Dropout – Randomly "drops out" (sets to zero) a fraction of neurons during training. This forces the network to avoid over-relying on specific neurons, promoting generalization.

3️⃣ Data Augmentation – Like giving different variants of questions to that friend so they get better at grasping concepts.

4️⃣ Early Stopping – Stop training before the model starts memorizing the data.

5️⃣ Batch Norm – Normalizes data (mean = 0, variance = 1) at each layer, ensuring all neurons get a fair chance in the next layer.

6️⃣ Elastic Net – A combination of L1 and L2 regularization.

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