The video provides a recap of deep neural networks, explaining their structure with input, hidden, and output layers. It describes dense network architecture where neurons in one layer connect to all neurons in the next, and sequential models where layers are stacked. Neurons perform linear aggregation and activation. The video concludes with a brief mention of building neural networks using TensorFlow.

What are the three main components of a deep neural network?

• Input layer, neuron layer, output layer

• Data layer, hidden layer, result layer

• Input layer, processing unit, output layer

• Input layer, hidden layer, output layer

In a dense network, how are neurons connected?

• Neurons are connected in a circular manner

• Each neuron is connected to every neuron in the next layer

• Neurons are connected to only one neuron in the next layer

• Neurons are connected randomly

What is a sequential model in the context of neural networks?

• A model where layers are randomly placed

• A model where layers are arranged in a circular fashion

• A model where layers are stacked in reverse order

• A model where layers are stacked one next to the other

What two operations are performed by neurons in a deep neural network?

• Signal transmission and reception

• Data collection and processing

• Input reception and output generation

• Linear aggregation and activation

What happens to data after it is processed in the hidden layers of a neural network?

• It is sent back to the input layer

• It moves to the output layer

• It is discarded

• It is stored for future use