Gradient Descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, it's commonly used to minimize the cost function of a model by updating the model's parameters.

Gradient Descent works by taking steps proportional to the negative of the gradient of the function at the current point. This process is repeated to find a local minimum of the function.

Key aspects of Gradient Descent include:

**Iterative Process**: Repeatedly updates parameters to minimize the cost function.**Learning Rate**: Determines the size of steps taken in each iteration.**Gradient Calculation**: Computes the gradient (partial derivatives) of the cost function.**Convergence**: Aims to reach a point where the gradient is close to zero.**Local vs. Global Minima**: May find local minima instead of the global minimum.

**Batch Gradient Descent**: Computes the gradient using the entire dataset.**Stochastic Gradient Descent (SGD)**: Updates parameters using only one sample at a time.**Mini-batch Gradient Descent**: Uses a small batch of samples to compute gradient updates.**Momentum**: Adds a fraction of the previous update to the current one.**Nesterov Accelerated Gradient**: A variation that provides a better approximation of future position.**AdaGrad**: Adapts the learning rate to the parameters.**RMSProp**: Adapts the learning rate using a moving average of squared gradients.**Adam**: Combines ideas from RMSProp and momentum.

**Efficiency**: Can efficiently optimize high-dimensional functions.**Scalability**: Works well with large datasets and complex models.**Flexibility**: Applicable to a wide range of optimization problems.**Online Learning**: Some variants allow for online learning with streaming data.**Simplicity**: Conceptually straightforward and easy to implement.

**Learning Rate Selection**: Choosing an appropriate learning rate can be challenging.**Local Minima**: May get stuck in local minima for non-convex functions.**Saddle Points**: Can slow down near saddle points in high-dimensional spaces.**Gradient Vanishing/Exploding**: Issues that can occur in training deep neural networks.**Computational Cost**: Computing gradients can be expensive for very large datasets.

**Learning Rate Scheduling**: Adjust the learning rate during training for better convergence.**Batch Normalization**: Normalize inputs to each layer to stabilize learning.**Gradient Clipping**: Limit the size of gradients to prevent exploding gradients.**Regularization**: Use techniques like L1/L2 regularization to prevent overfitting.**Momentum**: Implement momentum or related techniques to overcome local minima and plateaus.**Adaptive Learning Rates**: Use algorithms like Adam or RMSProp for adaptive learning rates.**Early Stopping**: Monitor validation performance to stop training when generalization stops improving.**Proper Initialization**: Initialize weights properly to aid convergence.

In linear regression:

- Start with initial parameter values.
- Compute the gradient of the cost function with respect to each parameter.
- Update parameters in the opposite direction of the gradient.
- Repeat steps 2-3 until convergence or a maximum number of iterations is reached.

The process gradually moves toward the optimal parameters that minimize the prediction error.

- Neural Networks: A set of algorithms inspired by the human brain that are designed to recognize patterns and process complex data inputs.
- Supervised Learning: A type of machine learning where the model is trained on labeled data, learning to map inputs to outputs.
- Fine-tuning: The process of further training a pre-trained model on a specific dataset to adapt it to a particular task or domain.
- Transfer learning: Applying knowledge gained from one task to improve performance on a different but related task.