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6. Hyperparameters in Deep Learning
Hyperparameters in deep learning are the configurations or settings that are set before the learning process begins and are not updated during training. They control the behavior of the training algorithm and the structure of the neural network, significantly impacting the performance and efficiency of the model.
Common Hyperparameters in Deep Learning:
1. Learning Rate (α):
Controls how much to adjust the weights of the network with respect to the loss gradient.
Example: A learning rate of 0.01 may cause the model to learn faster but might overshoot the optimal solution, while a learning rate of 0.0001 might be too slow, causing the model to take longer to converge.
2. Batch Size:
The number of training examples utilized in one iteration of the model.
Example: A batch size of 32 means the model processes 32 samples before updating the model parameters.
3. Number of Epochs:
The number of complete passes through the entire training dataset.
Example: Training a model for 50 epochs means the entire dataset is passed through the network 50 times.
4. Optimizer:
The algorithm used to update the weights of the network based on the computed gradients.
Example: Stochastic Gradient Descent (SGD), Adam, RMSprop.
5. Number of Layers:
The depth of the neural network, i.e., the number of layers in the model.
Example: A deep neural network might have 10 layers, while a shallow one might have only 3 layers.
6. Number of Neurons per Layer:
The number of neurons in each hidden layer of the network.
Example: A layer with 128 neurons vs. a layer with 512 neurons.
7. Dropout Rate:
A regularization technique where a percentage of neurons are randomly "dropped out" during training to prevent overfitting.
Example: A dropout rate of 0.5 means 50% of the neurons are dropped during each update cycle.
8. Activation Function:
The function used to introduce nonlinearity into the model.
Example: ReLU, Sigmoid, Tanh.
Example Scenario:
Suppose you're training a convolutional neural network (CNN) for image classification. You might choose a learning rate of 0.001, a batch size of 64, train it for 20 epochs, use the Adam optimizer, have 5 convolutional layers with 128 neurons each, and apply a dropout rate of 0.3 with a ReLU activation function.
These hyperparameters will dictate how the network learns and ultimately performs on the task.