Convolutional Neural Networks Overview
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Questions and Answers

What is the main challenge in creating a rule-based computational system for image classification?

  • Understanding consumer experiences depicted in the images (correct)
  • Determining the color channels in an image
  • Identifying the pixel values in images
  • Processing large volumes of labeled training data

    • In an RGB image, how many integers represent the value of each pixel?

  • Two integers per pixel
  • Four integers per pixel
  • One integer per pixel
  • Three integers per pixel (correct)

    • How is a greyscale image represented in terms of pixel values?

  • As a binary value of 0 or 1
  • As a single value ranging from 0 to 255 (correct)
  • Using three values, one for each color channel
  • As a vector of arbitrary length

    • What dimension does each RGB image in the dataset have?

    <p>227 x 227 x 3</p> Signup and view all the answers

    How many numbers are in the single dimensional vector representing each image?

    <p>154,587</p> Signup and view all the answers

    What is the primary function of the convolution operation in a Convolutional Neural Network?

    <p>To transform the input volume using a filter/kernel</p> Signup and view all the answers

    What is the purpose of padding in the context of convolutional layers?

    <p>To prevent information loss at the borders of images</p> Signup and view all the answers

    Which activation function is mentioned as commonly used in Convolutional Neural Networks?

    <p>ReLU</p> Signup and view all the answers

    What does the stride in a convolution operation determine?

    <p>The step-size of the filter as it moves</p> Signup and view all the answers

    What is the main purpose of pooling layers in CNNs?

    <p>To improve computational efficiency by reducing volume</p> Signup and view all the answers

    How many fully connected layers are used for classification in the described CNN application?

    <p>Two</p> Signup and view all the answers

    What is the result of using several convolutions on the same image in a CNN?

    <p>Extraction of different types of features</p> Signup and view all the answers

    Which operation is associated with max pooling in CNNs?

    <p>Taking the maximum of every block</p> Signup and view all the answers

    What is a primary challenge that arises with an increasing number of hidden layers in deep neural networks?

    <p>Harder optimization due to more weights to be learned</p> Signup and view all the answers

    Which of the following is a significant feature of Convolutional Neural Networks (CNN)?

    <p>Employs spatial correlations for grid-like data</p> Signup and view all the answers

    Which loss function is used in the training of a neural network?

    <p>Dependent on the specific task at hand</p> Signup and view all the answers

    What was a significant architectural feat achieved by Alexnet in the field of neural networks?

    <p>Won the ImageNet Large Scale Visual Recognition Challenge in 2012</p> Signup and view all the answers

    What does backpropagation in neural network training primarily utilize?

    <p>Gradient descent</p> Signup and view all the answers

    What does the term 'tied weights' refer to in the context of CNNs?

    <p>Weights shared among different neurons to reduce complexity</p> Signup and view all the answers

    Why is the learning rate significant in neural network training?

    <p>It affects the speed and quality of convergence</p> Signup and view all the answers

    What major change does CNN architecture implement compared to traditional neural networks?

    <p>Utilization of convolution and pooling operations</p> Signup and view all the answers

    What is a key step managers should take in relation to their brands?

    <p>Consider who the prominent authors of their brands are</p> Signup and view all the answers

    Which method is considered to be more accurate in understanding brand image?

    <p>Listening to consumer experiences on social media</p> Signup and view all the answers

    What is the primary classification problem in the data mining task mentioned?

    <p>Classifying consumer experiences into four classes</p> Signup and view all the answers

    What type of data is needed for training the model in the data mining task?

    <p>Consumer data related to the brands with class labels</p> Signup and view all the answers

    How do consumers typically communicate about brands on social media?

    <p>By posting images and tagging brands</p> Signup and view all the answers

    What are the identified classes for analyzing consumption experience in the data mining task?

    <p>Glamor, Fun, Healthy, and Rugged</p> Signup and view all the answers

    What social media platform is specifically mentioned for data collection related to brand images?

    <p>Instagram</p> Signup and view all the answers

    What is one of the steps managers need to take regarding brand authors?

    <p>Work to positively influence authors to deliver brand messages</p> Signup and view all the answers

    What is the first step in hyper-parameter tuning?

    <p>Create divisions of your data into train, validation, and test sets</p> Signup and view all the answers

    What is a purpose of comparing the average probabilities of each class across all images?

    <p>To understand brand image among consumers and the desired image</p> Signup and view all the answers

    What is K-Fold Cross Validation used for in hyper-parameter tuning?

    <p>To evaluate model performance with different subsets of data</p> Signup and view all the answers

    What does Randomized Search CV optimize for?

    <p>Time taken to evaluate hyperparameter values</p> Signup and view all the answers

    What should be done with the best hyperparameter values after identification?

    <p>Train the model using the training and validation sets</p> Signup and view all the answers

    Which of the following statements about hyperparameter tuning is false?

    <p>Hyperparameter tuning can only be done on the training set.</p> Signup and view all the answers

    What is the main advantage of using Grid Search CV?

    <p>It explores every possible combination of hyperparameters</p> Signup and view all the answers

    Why might a validation set not be representative of the data?

    <p>It is too small to capture the data distribution</p> Signup and view all the answers

    What is the benefit of combining training and validation data in k-fold cross-validation?

    <p>To ensure that the validation set has enough data representation</p> Signup and view all the answers

    What does obtaining the average probability of each class help to understand?

    <p>The consumer perception of the brand image</p> Signup and view all the answers

    Study Notes

    Convolutional Neural Networks

    • Can be considered to have two components: Feature Mappings and Classifier Layers.
    • Feature Mappings include Convolution, Activation functions (ReLU (max(0, 𝑥))), and Pooling.
    • Classifier is made up of Fully connected layers (neurons) with some activation function.
    • Convolution utilizes a filter/kernel that 'slides' along the input volume and transforms it.
    • The same weights are used for the entire input volume.
    • Stride is the step-size for the filter as it moves through the image (default value is 1).
    • Padding can be used to utilize pixels less on the image perimeter.

    Padding

    • Uses 0's padding in the border.
    • Performed especially in the final layers.

    Pooling

    • Transforms input volume by resizing it using operations such as min, max, avg.
    • Max pooling takes the maximum of every block.

    CNN Applications: Consumer Experience

    • Uses 5 convolution layers and Max Pooling.
    • Activation function: ReLU.
    • Two fully connected layers for classification.
    • Efficiently implemented using PyTorch library.

    Brand Image and Consumer Experiences

    • Brand image can be determined through customer surveys.
    • More accurately, listen to consumer experiences on Instagram and Twitter.
    • Consumers often tag brands in their posts.
    • Consumers communicate about brands with each other using these images.

    Image-Based Brand Image: Measuring Brand from Images

    • Technique: Convolution Neural Networks.
    • To know Brand Image: Simply conduct survey of the customers.
    • More accurate: Listen to consumer experiences through social media, such as Instagram, Twitter etc.

    Data Mining Task

    • Classify the consumption experience of apparel and beverage brands.
    • Classes: Glamor, Fun, Healthy, and Rugged.
    • Consider this task as image classification: 4 Class Classification problem.
    • Calculate average experience of all consumers in a given brand.

    Data for Model Building

    • Obtain tags from mTurk Annotators.
    • Build an image classifier to predict the experience.
    • Identify brand images by using brand hashtags in Instagram posts.
    • Brands: Adidas, Nike, Gucci, Levi's, Coca-Cola, Pepsi, Fanta, etc.

    Image Classification

    • Can identify the consumer experiences in these images.
    • The human visual system is incredibly good: the hallmark of intelligence.
    • Difficult to make a rule-based computational system that recognizes experiences in the images.

    Image Classification: ML Approach

    • Use a large number of labeled images to train a classifier.
    • Classify images as: Fun, Rugged, Glamor, Healthy.

    Image Representation

    • Each image is a matrix of pixel values.
    • Greyscale images: single value per pixel (0.0 represents black, 255 represents white).
    • RGB Image: Each pixel has three integers reflecting the contribution of red, green, blue color channels (0 to 255).
    • Dataset: 227 pixels wide and 227 pixels tall, 227 x 227 x 3.
    • Each image is represented by a single dimensional vector of RGB values.

    Neural Networks

    • Example: Multi-label classifier.
    • Each image is a 154,587 vector.

    Deep Neural Networks

    • Deep Neural Network: Increasing number of hidden layers since the 1980s.
    • Increasing the number of hidden layers results in more weight to learn, and optimization (Training) becomes harder.

    Convolution Neural Networks (CNN)

    • Utilizes spatial correlations in grid-like data (e.g. images).
    • CNN reduces the number of weights through convolution and pooling operations.

    CNN History: Alexnet

    • CNN designed by Krizhevsky, Sutskever and Hinton.
    • Won the ImageNet Large Scale Visual Recognition Challenge, 2012.
    • Marks the rise of deep neural networks.

    Image Net Challenge

    • Other architectures have improved performance further.

    Calculating Image-Based Brand Image

    • Obtain average probability of each class across all images of a brand shared by users and sponsors.
    • Compare the probabilities to understand the brand image among consumers and the brand image that the firm wishes to portray.

    Hyper-Parameter Tuning

    • Identify optimal value of parameters for the model.
    • Create 3 splits of your data: Train(70%), Validation(10%) & Test Sets (20%).
    • Use different hyperparameter values and train different models using Training Data.
    • Evaluate performance of each model on Validation Set and choose the best hyper parameters.
    • One validation set may not be representative of the data, combine training and validation data and partition them into "k" folds.

    K-Fold Cross Validation

    • Divide entire data into K folds.
    • Train on training set and evaluate on validation set.
    • Average performance over all the folds to estimate "true" test error.

    Hyper-Parameter Tuning

    • Train model on Training + Validation set to learn a new model.
    • Grid SearchCV: Evaluates every possible combination of hyper-parameter values.
    • Randomized search CV: Random samples of hyperparameter values are evaluated.
    • Existing packages (sklearn) perform these efficiently.

    References

    • Churn management:
    • Neural Networks:
    • Hyper-Parameter Tuning:

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    Description

    This quiz explores the fundamental components of Convolutional Neural Networks (CNNs), including feature mappings, classifier layers, and the roles of convolution, activation functions, and pooling. Additionally, it covers applications and design considerations involved in optimizing CNN architecture. Test your knowledge of how CNNs function and their significance in deep learning.

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