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Describe briefly the training process of a Neural Network model

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For a complete understanding of Neural Network, check out the post: Basic architecture of Neural Network, Training Process, and Hyper-parameters tuning

Training of a Neural Network Model

Neural Networks, in true sense, are just mathematical expressions which take the training data and weights as inputs and emits predictions and loss values as outputs.

To train a neural network model, the initial step involves collecting and preprocessing the input data (such as cleaning, normalizing, etc.) to prepare it for training. Next, determine the architecture of your neural network. This involves choosing layer types (such as convolutional, recurrent, or transformer layers), deciding on the number of layers and neurons within each layer, initializing weights, and selecting activation functions, loss function, regularization technique and the optimization algorithm.

In the training process, a mini-batch of training data is sampled during each iteration. Each batch goes through a training loop. A typical training loops looks like this:

  1. Sample a mini-batch of data from the training dataset
Title: A simple neural network, an illustrative example
Source: Research
  1. Forward Propagation of data through the hidden layers
    • Calculate weighted sum of inputs at each neuron
    • Apply activation function to the weighted sum
    • Predict output and calculate loss (difference between predicted and actual values)
Forward propagation in a Neural Network
Title: Forward propagation in a Neural Network
Source: Research
  1. Backpropagation
    • Calculate gradients of the loss with respect to all model parameters
    • Update the network weights using an optimization algorithm
 Backpropagation in a Neural Network
Title: Backpropagation in a Neural Network
Source: Research

The process of iteratively adjusting the weights based on gradients aims to minimize the loss function and improve the model’s performance on the training data. This training loop continues until the predefined stopping criteria are met, which could be a fixed number of epochs, early stopping based on validation performance, or other convergence indicators.

During the training process, the model’s performance is periodically tested on a separate validation set to check how well the model is generalizing to new data. Hyperparameters such as learning rate, batch size are adjusted based on validation performance. Once the training is complete, the trained model is tested on test dataset and the performance is reported.

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