Describe a neural network : Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks)

The video tutorial introduces artificial and deep neural networks, explaining their biological basis and machine learning implementations. It covers the backpropagation algorithm for training neural networks and introduces common algorithms like perceptrons and multilayer perceptrons. The tutorial concludes with a brief overview of deep neural networks and future learning topics.

What is the role of dendrites in a biological neural network?

• To transmit electrical impulses to other neurons

• To receive stimuli from the external environment

• To connect neurons to muscles

• To store information

In a neural network, what determines if a neuron 'fires'?

• The number of layers in the network

• The number of inputs it receives

• The strength of the nerve impulse

• The type of activation function used

What is the primary purpose of the backpropagation algorithm?

• To remove unnecessary neurons

• To increase the speed of computation

• To adjust weights to minimize error

• To add more layers to the network

Which neural network algorithm is known for not having any hidden layers?

• Convolutional Neural Network

• Perceptron

• Deep Neural Network

• Multilayer Perceptron

What is a key feature of multilayer perceptrons?

• They only handle linear data

• They do not require training

• They use a single layer of neurons

• They utilize non-linear activation functions

What distinguishes deep neural networks from basic neural networks?

• They are only used for image processing

• They require less data for training

• They do not use activation functions

• They have more than one hidden layer

Which framework is mentioned for implementing deep neural networks in the course?

• TensorFlow

• PyTorch

• H2O

• Keras