Week 13 COE305 Machine Learning

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Questions and Answers

What does ANN stand for?

Artificial Neural Networks

What are the four key concepts within Artificial Intelligence?

Artificial Intelligence, Machine Learning, Neural Networks, Deep Learning

What is the role of the Perceptron in the understanding of ANNs?

The Perceptron helps us understand the concept of Artificial Neural Networks.

Artificial Neural Networks are based on the structure of the human brain.

True Signup and view all the answers

Which of the following is NOT a component of an Artificial Neural Network?

Activation Furtion Signup and view all the answers

Which of the following is a key algorithm used in Machine Learning?

Decision Tree Signup and view all the answers

Which of the following is NOT a characteristic of Machine Learning (ML) compared to Artificial Neural Networks (ANN)?

ML models have a black box nature, making them harder to interpret. Signup and view all the answers

What is the name of the most basic type of neural network?

Single Layer Neural Network / Perceptron Signup and view all the answers

What is the purpose of hidden layers in a neural network?

Hidden layers are layers of neurons that are not directly connected to the input or output layers. Signup and view all the answers

A dense layer in a neural network involves each node being connected to every node in the next layer?

True Signup and view all the answers

What is the purpose of an optimizer in a neural network?

Optimizers are algorithms that help find the best set of weights and biases for the neural network by minimizing the error or loss function. Signup and view all the answers

What are the three layers commonly found in a Neural Network?

Input Layer, Hidden Layer, Output Layer Signup and view all the answers

Why are weights assigned to each feature in a neural network?

Weights are assigned to features because not all features are equally important for the prediction. Signup and view all the answers

What is the purpose of the bias term in the activation function of a neuron?

The bias allows the activation function to be shifted left or right to better fit the data. Signup and view all the answers

Which activation function maps values between 0 and 1?

Sigmoid or Logistic Signup and view all the answers

Which activation function is known to be commonly used in image recognition?

ReLU Signup and view all the answers

The ReLU activation function allows output values to be negative, making it suitable for tasks requiring a wider range of outputs.

False Signup and view all the answers

What is the purpose of batch size in the training process of a neural network?

Batch size determines the number of samples used for updating the model's weights in each iteration of training. Signup and view all the answers

What is the primary purpose of backpropagation in neural networks?

Backpropagation is an algorithm used to calculate the gradient of the loss function with respect to the weights and biases in a neural network. Signup and view all the answers

What does the learning rate represent in the context of a neural network's training?

The learning rate controls the step size of weight adjustments during each iteration of training, determining how much the model's weights are changed in response to the error. Signup and view all the answers

What is the formula used to calculate the error in a Feedforward Neural Network?

error = -(ylog(ŷ) + (1-y)log(1-ŷ)) Signup and view all the answers

Study Notes

Week 13 COE305 Machine Learning

Course covers Artificial Neural Networks (ANN)
ANNs mimic human behavior through neuron activation
ANNs use artificial neurons that interconnect
Perceptron is a fundamental ANN concept
Perceptrons allow multiple inputs to create a single output (e.g., loan approval)
ANNs were theorized in the 1950s
ANN structure is based on the human brain's biological neural network
ANNs don't use activation functions, only nodes
ANNs have inputs, nodes, weights, output

ML vs ANN

Machine Learning (ML): A subset of AI focusing on algorithms learning from data. It includes various methods like supervised, unsupervised, and reinforcement learning, encompassing simpler algorithms like regression and decision trees.
Artificial Neural Networks (ANN): A subset of ML inspired by biological neural networks. ANNs focus on neural network-based approaches, often more complex with multiple layers and parameters.
Data Requirements: ML performs well with smaller to medium-sized datasets, while ANNs require significantly larger datasets to effectively learn.
Interpretability: ML models are generally easier to interpret compared to ANNs, which can be more challenging to understand due to their complexity.
Applications: ML finds use in predictive analytics, fraud detection, recommendation systems, and basic classifications. ANNs are crucial for image recognition, speech recognition, and autonomous vehicles.
Training Time: Simple ML models typically train faster compared to intricate ANN models.
Key Algorithms: Linear regression, logistic regression, decision trees are examples of common ML algorithms. Feedforward NN, Convolutional NN, and Recurrent NN. are examples of ANN.
Computational Power: ML benefits from lower computational power for simple models. Compared to ANNs, which often require substantial computational resources, particularly for complex deep learning applications.

Basic Architecture of Neural Networks

Neural networks consist of interconnected nodes, which can be represented with numbers, like salary, tenure etc.
The network's structure includes input nodes, hidden layers, and output nodes. They help with complex processing.
Input layer receives information.
Hidden layers process the received data using summation and activation functions; they connect to other layers and nodes. The hidden layer's main function is to process data received from the inputs, using a function-activation, and transmit it to the output layer to complete the task using other layers.
The output layer outputs the results; it's the final stage.
Connections between nodes are weighted to modulate signals.
Input values are multiplied with weights, then combined, and passed through an activation function to get the network's output (e.g. affordability example).

Components of Neural Networks

Layers: Input, hidden layers, output, multiple layers help complex processing
Weights: Adjust the importance of inputs in determining the output.
Bias: Shifts the activation function to fit the provided data; it adjusts the decision boundary to improve the model's accuracy.
Activation Functions: Functions applied to the weighted sum of inputs, defining the output values, crucial for non-linear operations. Popular activation functions include sigmoid, ReLU, tanh.

Activation Functions

Threshold/Binary Step: Output is 1 if input > 0, else 0.
Sigmoid/Logistic: Transforms values between 0 and 1, useful for classification.
ReLU (Rectified Linear Unit): Output = input if > 0, else 0.
Tanh (Hyperbolic Tangent): Output values between -1 and 1.
Leaky ReLU: A variant of ReLU that allows a small but non-zero output for negative inputs.

Feed-forward Neural Network (FFNN)

FFNNs have a direct path from input to output without loops.
Input values are weighted and summed.
The activation function processes the sum, determining the output.
Errors are calculated, and weights are modified via backpropagation to decrease error.

Neural Network with Backpropagation

Backpropagation adjusts weights to minimize errors in the model's predictions by learning from previous errors to improve predictions in the future.
The "learning rate" determines the step size during weight adjustments to reach minima.
Used after the activation function completes; it's an iterative process.

Total Error Calculation

The total error measures the differences between predicted and actual values across all instances in the dataset by adding up errors for each instance, and it's a way to quantify the inaccuracy of the models, indicating the model's capability.
Calculation is based on an error function, such as log loss in binary classification problems.

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Description

This quiz covers key concepts of Artificial Neural Networks (ANN) as part of the COE305 Machine Learning course. It explores how ANNs mimic human behavior through neuron activation and details their structure, including perceptrons. Additionally, it contrasts Machine Learning with ANN, detailing their different focuses and data requirements.