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

The video tutorial explains the process of training recurrent neural networks (RNNs). It covers the training routine, including the forward step, loss computation, gradient computation, and parameter updates. The tutorial also demonstrates how to implement the update parameters function and run the training function. Finally, it discusses applying RNNs to real-life natural language processing problems, such as sentiment classification using Yelp reviews.

What is the purpose of the learning rate in the training routine?

• To determine the size of the input embeddings

• To update the parameters during gradient descent

• To set the number of epochs

• To initialize the weight matrices

In the context of neural networks, what does the forward step involve?

• Setting gradients to zero

• Calculating the loss

• Updating the parameters

• Generating predictions from input data

What is the role of the backward step in training neural networks?

• To update the learning rate

• To compute the loss

• To calculate gradients for parameter updates

• To initialize the network

Why is it important to set gradients to zero before the next epoch?

• To decrease the number of epochs

• To increase the learning rate

• To prevent accumulation of gradient information

• To initialize the weight matrices

What does the update parameters function require?

• The target vectors

• The input embeddings

• The original parameters and their gradients

• Only the learning rate

What is observed when the training function is executed over multiple epochs?

• The loss increases

• The loss remains constant

• The loss fluctuates randomly

• The loss decreases

What is the primary goal of the training function?

• To set the learning rate

• To initialize the network

• To reduce the loss over epochs

• To compute the loss

In the context of the video, what is the next step after training the model?

• Changing the learning rate

• Increasing the number of epochs

• Applying the model to predict the next word in a sequence

• Testing the model on a new dataset

What kind of problem is introduced as a real-life example in the video?

• Object detection

• Speech recognition

• Sentiment classification

• Image classification

What is the assumed knowledge for the next module on sentiment classification?

• Understanding of automatic differentiation

• Experience with object detection

• Familiarity with speech recognition

• Knowledge of image processing