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.

Explain the concept of back propagation in neural networks.

Describe the process of importing libraries necessary for building the model.

What is the purpose of the Keras library in building neural networks?

What are the two classes of speed indicated in the data?

Explain how the scatterplot is used to visualize the data.

What are the two inputs used in the model discussed in the text?

What is the significance of the bias in a neural network model?

What is the role of the activation function in the model?

How does the model summary help in understanding the neural network?

How do you initialize weights and biases in the model?