Week 5 Quiz
Which of the following statement(s) correctly represents a real neuron? Group of answer choices A neuron has a single input and a single output only A neuron has multiple inputs but a single output only A neuron has a single input but multiple outputs A neuron has multiple inputs and multiple outputs All of the above statements are valid
A neuron has multiple inputs but a single output only
Artificial neural networks are not modeled from biological neurons.
False
Circles on a Neural Net Schematic Diagram are called c Nodes.
False
These are the different Layers in an ANN: Input Layer: This is the layer that receives the input data and passes it on to the next layer. The input layer is typically not counted as one of the hidden layers of the network. Hidden Layers: The input layer is the one that receives input data and transfers it to the next layer. Usually, the input layer is not included in the list of the hidden layers of the neural network. Output Layer: This is the output-producing layer of the network. A binary classification problem might only have one output neuron, but a multi-class classification problem might have numerous output neurons, one for each class. The number of neurons in the output layer depends on the type of problem being solved.
False
You are building a neural network where it gets input from the previous layer as well as from itself Which of the following architecture has feedback connections? Group of answer choices Recurrent Neural network Convolutional Neural Network Transformer Restricted Boltzmann Machine
Recurrent Neural network
A neuron takes a group of weighted inputs, applies an activation function, and returns an output.Inputs to a neuron can either be features from a training set or outputs from a previous layer's neurons. Weights are applied to the inputs as they travel along synapses to reach the neuron. The neuron then applies an activation function to the "sum of weighted inputs" from each incoming synapse and passes the result on to all the neurons in the next layer
True
Advantages of Neural Networks -good predictive performance-they are known to have high tolerance to noisy data and the ability to capture highly complicated relationships between the predictors and the response.
True
Bias- are additional constants attached to neurons and added to the weighted input before the activation function is applied. Bias terms help models represent patterns that do not necessarily pass through the origin.
True
Neural networks are a class of machine learning algorithms used to model complex patterns in datasets using multiple hidden layers and non-linear activation functions. A neural network takes an input, passes it through multiple layers of hidden neurons (mini-functions with unique coefficients that must be learned), and outputs a prediction representing the combined input of all the neurons.
True
Synapse- Synapses are like roads in a neural network. They connect inputs to neurons, neurons to neurons, and neurons to outputs. In order to get from one neuron to another, you have to travel along the synapse paying the "toll" (weight) along the way. Each connection between two neurons has a unique synapse with a unique weight attached to it. When we talk about updating weights in a network, we're really talking about adjusting the weights on these synapses.
True
