Artificial Intelligence and Machine Learning: Training Neural Networks for Image Classification
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Questions and Answers

What is the primary issue with extracting only pixel values as features?

  • Insufficient data
  • Unstructured data
  • High dimensionality (correct)
  • Non-linear decision boundary
  • Which type of dataset is logistic regression and softmax regression suitable for?

  • Unstructured data
  • Linearly separable data (correct)
  • High-dimensional data
  • Non-linearly separable data
  • What is the primary advantage of a single perceptron?

  • Ability to learn complex representations
  • Ability to handle high-dimensional data
  • Ability to handle non-linearly separable data
  • Simplicity and powerful problem-solving capabilities (correct)
  • What is the primary limitation of decision trees and support vector machines for image classification?

    <p>Inability to handle unstructured data</p> Signup and view all the answers

    What is the name of the neural network architecture described in the lecture?

    <p>Fully Connected Neural Network</p> Signup and view all the answers

    What is the inspiration for the design of perceptrons?

    <p>Biological neurons</p> Signup and view all the answers

    What is the limitation of using a single layer of perceptrons?

    <p>It is unable to find the optimal boundary that separates classes</p> Signup and view all the answers

    What is the main advantage of using a multi-layer perceptron over a single layer perceptron?

    <p>It can learn non-linear decision boundaries</p> Signup and view all the answers

    What is the characteristic of a fully connected neural network?

    <p>Every edge has its own weight value</p> Signup and view all the answers

    Why is a single layer neural network called a single layer?

    <p>Because it has only one layer of neurons</p> Signup and view all the answers

    What is the problem with using a single layer of perceptrons to solve the XOR problem?

    <p>It is unable to solve the XOR problem because of its linear nature</p> Signup and view all the answers

    What is the condition for a perceptron to converge to an optimal solution?

    <p>The problem has a linearly separable solution</p> Signup and view all the answers

    What is the one hot encoded representation of the image of a cat?

    <p>[1, 0, 0]</p> Signup and view all the answers

    Why is the loss function high in the given example?

    <p>The model is confused between the image being a cat or a dog.</p> Signup and view all the answers

    What is the purpose of computing gradients in a multi-layer perceptron?

    <p>To update the weights of the network</p> Signup and view all the answers

    What is the role of forward and backward propagation in a multi-layer perceptron?

    <p>Forward and backward propagation are used together to update the weights.</p> Signup and view all the answers

    What is the example application mentioned in the context of multi-layer perceptron?

    <p>Hand Written Digit Recognition</p> Signup and view all the answers

    What is the formula for calculating the loss function?

    <p>𝑪 = − 𝒚𝒊 𝒍𝒐𝒈𝒊</p> Signup and view all the answers

    What is the main purpose of the error function E in a neural network?

    <p>To define the error between the desired output and calculated output</p> Signup and view all the answers

    What does the subscript k denote in a neural network?

    <p>The output layer</p> Signup and view all the answers

    What is the main difference between stochastic gradient descent and batch gradient descent?

    <p>The amount of data used to compute the gradient</p> Signup and view all the answers

    What is the trade-off in using more data to compute the gradient of the objective function?

    <p>Increased accuracy but slower convergence</p> Signup and view all the answers

    What is the update rule for batch gradient descent?

    <p>𝒘 = 𝒘 - 𝜶𝜵𝒘 𝑱(𝜽)</p> Signup and view all the answers

    What is the main advantage of using mini-batch gradient descent?

    <p>A trade-off between computation speed and accuracy</p> Signup and view all the answers

    What is the purpose of annealing in machine learning?

    <p>To reduce the learning rate according to a pre-defined schedule</p> Signup and view all the answers

    What is the recommended approach when the error stops decreasing during mini-batch learning?

    <p>Turn down the learning rate to reduce fluctuations in the final weights</p> Signup and view all the answers

    What is the benefit of using a separate validation set to monitor the error during training?

    <p>It provides a more accurate estimate of the model's performance on unseen data</p> Signup and view all the answers

    What is the approach described in the lecture for adjusting the learning rate during mini-batch gradient descent?

    <p>Guess an initial learning rate and adjust it based on the error</p> Signup and view all the answers

    What is the purpose of reducing the learning rate towards the end of mini-batch learning?

    <p>To reduce the fluctuations in the final weights</p> Signup and view all the answers

    What is the recommended strategy when the error is falling fairly consistently but slowly during training?

    <p>Increase the learning rate to speed up convergence</p> Signup and view all the answers

    Study Notes

    Challenges in Image Classification

    • Challenge 1: Extraction of Features: High-dimensional dataset (e.g., 784 columns for 28x28 images) makes it difficult to extract relevant features.
    • Challenge 2: Non-Linear Decision Boundary: Logistic Regression is limited to linearly separable data, and more advanced algorithms like Decision Trees and SVM are not suitable for unstructured datasets like images.

    Architecture of Neural Networks

    • Fully Connected Neural Network
    • Single Unit of Perceptron (Neuron): inspired by human neurons, with the same representation capacity as logistic regression.

    Limitations of Single Unit of Neuron

    • Limited to linearly separable solutions, cannot find optimal boundary for non-linearly separable data
    • XOR problem is an example of a limitation

    Multi-Layer Neural Network

    • Putting neurons in layers to create a Multi-Layer Neural Network
    • Single Layer Fully Connected Neural Network: every edge has its own weight value

    Neural Architecture: Single Layer Neural Network

    • Fully Connected Network: each neuron is connected to every other neuron in the previous and next layers
    • Single Layer: only one layer of neurons between input and output
    • Number of neurons in each layer: depends on the problem and dataset

    Neural Architecture: Multi-Layer Neural Network

    • Architecture: Multi-Layer Fully Connected Neural Network

    One Hot Encoding

    • Representing categorical output labels as binary vectors (one hot encoding)

    Loss Function

    • Calculates the difference between predicted output and actual output
    • Goal: minimize the loss function

    Computing Gradients

    • Forward and Backward Propagations: used to learn weights in Multi-Layer Networks
    • Forward Propagation: computes the output of the network
    • Backward Propagation: computes the gradients of the loss function with respect to the weights

    Backpropagation Algorithm

    • Computes gradients of the loss function with respect to the weights
    • Used to update the weights in the network

    Variants of Gradient Descent

    • Batch Gradient Descent: uses the entire dataset to compute the gradient
    • Stochastic Gradient Descent: uses a single data point to compute the gradient
    • Mini-Batch Gradient Descent: uses a subset of the dataset to compute the gradient

    Batch Gradient Descent

    • Computes the gradient of the cost function with respect to the parameters for the entire training set
    • Update rule: subtracts the product of the learning rate and the gradient from the current weights
    • Annealing: reducing the learning rate according to a pre-defined schedule or threshold

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    Description

    This quiz covers the challenges of training neural networks for image classification, including feature extraction and high-dimensional datasets. It focuses on multi-layer perceptrons and their applications in image recognition. Test your understanding of AI and machine learning concepts.

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