Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Architecture

The video introduces the concepts of neurons, bias, and activation functions in neural networks. It explains how neurons are connected to form a network and the role of hyperparameters in training. The structure of deep neural networks is explored, focusing on fully connected and feedforward networks. The video concludes with a discussion on implementing these concepts and hints at future topics like activation functions and bias terms.

What is the role of neurons in a neural network without activation functions and bias terms?

• To store data temporarily

• To process input data and produce output

• To act as a memory unit

• To perform data encryption

What is a hyperparameter in the context of neural networks?

• A parameter that is adjusted during training

• A parameter that is set before training begins

• A parameter that is irrelevant to training

• A parameter that is automatically optimized

How are neurons connected in a fully connected layer?

• Each neuron is connected to neurons in the same layer

• Neurons are not connected to any other neurons

• Each neuron is connected to only one neuron in the previous layer

• Each neuron is connected to all neurons in the previous layer

What does the term 'feedforward' refer to in neural networks?

• The process of data moving backward through the network

• The process of data moving forward through the network

• The process of data being stored in the network

• The process of data being deleted from the network

What is the next step after implementing a deep neural network without activation functions and bias terms?

• To remove layers

• To understand the purpose of activation functions

• To add more layers

• To finalize the network structure