Neural Network Training Quiz

Artificial Neural Network Training

• The main objective of training an artificial neural network is to optimize the model's weights to make accurate predictions.

Optimization Algorithms

• The algorithm that optimizes the weights during training is called an optimizer.
• The most widely used optimizer is Stochastic Gradient Descent (SGD).

Stochastic Gradient Descent (SGD)

• The objective of SGD is to minimize the loss by adjusting the model's weights.
• SGD updates the model's weights during each iteration to reduce the loss.
• The purpose of SGD is to iteratively adjust the model's weights to make accurate predictions.

Model Training Process

• During the forward pass, the model makes predictions about an image by propagating input through the network.
• The loss function calculates the difference between the model's predictions and the actual output.
• The loss represents the error between the model's predictions and the actual output.

Training Process

• During training, the same data is repeatedly sent through the network to update the model's weights.
• The purpose of repeatedly sending the same data is to iteratively adjust the model's weights to minimize the loss.

Deep Learning Practitioners

• The primary responsibility of deep learning practitioners is to design and optimize loss functions to achieve the desired model performance.

Model Learning

• The model learns from the data through the process of SGD, where the model iteratively adjusts its weights to minimize the loss.
• The purpose of the model learning from the data is to make accurate predictions.