Create a computer vision system using decision tree algorithms to solve a real-world problem : Code to build a perceptro

The video tutorial guides viewers through building a simple perceptron model using Keras and TensorFlow. It begins with an introduction to the perceptron model, followed by importing necessary libraries like pandas, numpy, matplotlib, and seaborn for data manipulation and visualization. The tutorial then demonstrates loading and visualizing a sample dataset, preparing it for training, and constructing the perceptron model in a sequential manner. Finally, it reviews the model summary, including the initialization of weights and biases.

Which library is primarily used for statistical data visualization in the tutorial?

• Pandas

• NumPy

• Seaborn

• Matplotlib

What is the main advantage of using Keras over building neural networks from scratch?

• It requires less data.

• It simplifies the process significantly.

• It provides more accurate results.

• It is faster to execute.

What type of data is used in the tutorial for classification?

• Real-world data

• Image data

• Synthetic data

• Time-series data

What does the 'hue' parameter in the scatter plot function indicate?

• The color representing different classes

• The size of the points

• The transparency of the points

• The shape of the points

What are the two inputs used in the perceptron model?

• Distance from bump and speed

• Speed and time

• Height of bump and speed

• Distance from bump and height of bump

In the perceptron model, what is the purpose of the 'dense' layer?

• To normalize the input data

• To add dropout regularization

• To create a fully connected layer

• To reduce the dimensionality of data

How many tunable parameters are there in the simple perceptron model?

• Five

• Two

• Four

• Three

What is the initial value of the bias in the perceptron model?

• -0.5

• 0

• 1

• 0.5

What method is used to get a summary of the model in Keras?

• model.details()

• model.info()

• model.describe()

• model.summary()

What is the role of the 'get_weights' method in the tutorial?

• To update the weights during training

• To delete the weights

• To retrieve the current weights and biases

• To initialize the weights