Data Science and Machine Learning (Theory and Projects) A to Z - RNN Implementation: Language Modelling Next Word Predic

The video tutorial explains how to define and compute the loss function using cross entropy loss. It covers the process of calculating loss for model predictions and one-hot vectors, and introduces the compute loss function. The tutorial also prepares viewers for writing a train function that uses gradient descent to minimize loss, with automatic gradient computation using PyTorch's autograd mechanism.

What is the primary purpose of defining a loss function in machine learning?

• To determine the model's complexity

• To calculate the model's speed

• To optimize the model's performance

• To measure the accuracy of predictions

In the context of cross-entropy loss, what does the negative log of predicted values represent?

• The probability of incorrect predictions

• The confidence level of predictions

• The likelihood of correct predictions

• The error rate of the model

How is the expected loss calculated from the total loss?

• By multiplying the total loss by the number of predictions

• By dividing the total loss by the number of predictions

• By subtracting a constant from the total loss

• By adding a constant to the total loss

What is the role of gradient descent in training a model?

• To maximize the prediction accuracy

• To reduce the model's speed

• To minimize the loss function

• To increase the model's complexity

Which mechanism is mentioned for automatic gradient computation?

• Scikit-learn gradient

• Keras backend

• TensorFlow optimizer

• Torch autograd