Deep Learning - Crash Course 2023 - Training the Neural Network

The video tutorial covers the process of importing necessary libraries and setting up a 2D classification dataset. It explains how to split the dataset into training and test sets, and how to prepare a deep learning model by adjusting the input shape to match the dataset's features. The model is defined with two hidden layers and compiled using the RMSprop optimizer. The training process is demonstrated over 1000 epochs, showing improvements in accuracy and reductions in loss. The video concludes with a summary of the model's performance and hints at future learning topics.

What are the two features used in the 2D classification dataset?

• Height and Weight

• X and Y coordinates

• Age and Income

• Temperature and Humidity

What is the purpose of splitting the dataset into training and test sets?

• To evaluate the model's performance on unseen data

• To improve the speed of the model

• To reduce the size of the dataset

• To increase the number of features

Why was the input shape of the model modified?

• To increase the number of neurons

• To add more layers to the model

• To match the number of features in the dataset

• To change the activation function

Which optimizer is used in the model compilation?

• Adagrad

• RMSprop

• SGD

• Adam

What activation function is used in all layers of the model?

• ReLU

• Tanh

• Softmax

• Sigmoid

How many epochs were used to train the model?

• 1000

• 2000

• 500

• 1500

What was the final accuracy on the validation data?

• 100%

• 85%

• 90%

• 96.67%