What is stochastic gradient descent?

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🔑 Stochastic Gradient Descent (SGD)

Gradient Descent is an optimization algorithm used to minimize a loss function (or cost function) in machine learning models. It works by updating the model parameters (weights) in the direction of the negative gradient (steepest descent) of the loss function.

Stochastic Gradient Descent (SGD) is a variation where, instead of computing the gradient using the entire dataset (which can be very large and computationally expensive), we update the model parameters using only one randomly chosen sample (or a small batch) at a time.

⚡ How it Works

1. Initialize model parameters (weights).

2. Randomly pick one training example (or a mini-batch).

3. Compute the gradient of the loss function with respect to parameters for that sample.

4. Update parameters:

$$\theta := \theta - \eta \cdot \nabla L(\theta; x_i, y_i)$$

where

• $\theta$ = model parameters

• $\eta$ = learning rate

• $\nabla L$ = gradient of loss for sample $(x_i, y_i)$

5. Repeat for many iterations until convergence.

🎯 Why “Stochastic”?

Because each parameter update is based on a random sample, the process is noisy and does not strictly follow the exact gradient. This randomness helps the model escape local minima and improves generalization.

✅ Advantages of SGD

• Much faster than batch gradient descent on large datasets.

• Can generalize better due to noise in updates.

• Suitable for online learning (model updates continuously as new data arrives).

⚠️ Disadvantages

• Updates are noisy, which can make convergence unstable.

• Requires careful tuning of the learning rate.

• May oscillate around the minimum instead of converging smoothly.

🧠 In short:

Stochastic Gradient Descent (SGD) is an optimization method where model parameters are updated incrementally using one (or a few) random training samples at a time. It’s widely used in training deep learning models because it is efficient, scalable, and effective on large datasets.

Read more :

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