3 a] Explain the working of deep forward networks.
A Deep Forward Network (DFN), also known as a deep neural network, is a type of artificial neural network that maps input data to output predictions through multiple layers. Here’s how it works conceptually, step by step:
1. Input Layer: Receiving Data
- The network starts with an input layer where raw data, like an image or numerical values, is fed into the network.
- Each feature of the data corresponds to a node (or neuron) in the input layer.
2. Hidden Layers: Processing the Data
- After the input layer, the data passes through multiple hidden layers. These layers are where most of the computations and transformations happen.
- Each hidden layer consists of neurons, and each neuron is connected to neurons in the previous and next layers.
- The connections have weights that determine the importance of the input values. These weights are adjusted during training to improve predictions.
3. Activation Functions: Adding Non-Linearity
- After summing the weighted inputs, an activation function is applied to each neuron’s output.
- This step introduces non-linearity, allowing the network to learn complex patterns and relationships in the data.
4. Output Layer: Producing Predictions
- The final layer is the output layer, which produces the result. For example:
- A single output neuron for regression problems (like predicting house prices).
- Multiple output neurons for classification tasks (like identifying whether an image contains a cat or a dog).
5. Forward Propagation: Data Flow
- The data flows forward through the network, from the input layer to the output layer, passing through the hidden layers. This process is called forward propagation.
6. Learning Through Feedback
- During training, the network compares its predictions with the actual target values using a loss function (a measure of error).
- The loss is then used to update the weights in the network using a process called backpropagation:
- The error is propagated backward through the network to compute how much each weight contributed to the error.
- An optimization algorithm (like gradient descent) adjusts the weights to reduce the error.
7. Iteration and Training
- This forward and backward process is repeated across multiple iterations (epochs) with a dataset until the network learns to make accurate predictions.
8. Generalization
- Once trained, the network can process new, unseen data and make predictions based on the patterns it learned during training.