Week 14: Building Neural Networks
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Questions and Answers

What is the goal of backpropagation in neural networks?

To adjust the weights so that the neural network produces the optimized output.

What is the purpose of an optimizer in machine learning?

To adjust the weights of a neural network during training to minimize the loss function.

Which of the following is NOT a type of optimizer?

  • Gradient Descent
  • Stochastic Gradient Descent
  • Mini-batch SGD
  • Random Forest (correct)

Adam is a good choice for deep networks due to its adaptability.

<p>True (A)</p> Signup and view all the answers

What is a loss function?

<p>A mathematical function that measures the difference between the predicted output of a machine learning model and the actual target values (ground truth).</p> Signup and view all the answers

Which of these is NOT a type of loss function?

<p>Rectified Linear Unit (ReLU) (A)</p> Signup and view all the answers

Who is considered the pioneer of convolutional neural networks?

<p>Yann LeCun</p> Signup and view all the answers

What was the first convolutional neural network called?

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

Convolutional Neural Networks (CNNs) are specifically designed for image processing.

<p>True (A)</p> Signup and view all the answers

Which of the following is NOT a typical application area of CNNs?

<p>Financial Modeling (A)</p> Signup and view all the answers

What are the main layers in a Convolutional Neural Network?

<p>Convolution (A), Activation (B), Pooling (C)</p> Signup and view all the answers

Convolutional layers are not sensitive to the location of features in the input.

<p>False (B)</p> Signup and view all the answers

What is the purpose of pooling layers?

<p>To downsample feature maps, reducing dimensionality and spatial invariance while maintaining important features.</p> Signup and view all the answers

What are the two main types of pooling layers?

<p>Average pooling (C), Max pooling (D)</p> Signup and view all the answers

Pooling is performed on each channel of the feature map separately.

<p>True (A)</p> Signup and view all the answers

What is the role of the fully connected layer in a CNN?

<p>To classify the extracted features and produce the final output prediction.</p> Signup and view all the answers

What does the LeNet-5 architecture consist of?

<p>Two sets of convolutional and average pooling layers, a flattening convolutional layer, two fully-connected layers, and a softmax classifier.</p> Signup and view all the answers

Which of the following is NOT a variant of CNNs?

<p>Long Short-Term Memory (LSTM) (D)</p> Signup and view all the answers

What is a key feature of AlexNet that made it a breakthrough in image recognition?

<p>The use of ReLU activations, dropout, and overlapping max pooling.</p> Signup and view all the answers

What are the most common sizes of convolutional filters used in VGGNet?

<p>3x3</p> Signup and view all the answers

GoogLeNet utilizes inception modules that perform convolutions with a single filter size in parallel.

<p>False (B)</p> Signup and view all the answers

What is the main purpose of "skip connections" or "residual blocks" in ResNet?

<p>To avoid vanishing gradients in deep networks.</p> Signup and view all the answers

DenseNet features sparse connections between layers.

<p>False (B)</p> Signup and view all the answers

What is the primary advantage of MobileNet?

<p>It is designed for mobile and embedded devices and uses depthwise separable convolutions for efficiency.</p> Signup and view all the answers

What does EfficientNet aim to optimize by balancing model depth, width, and resolution?

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

What is the goal of R-CNN?

<p>To extract region proposals for object detection.</p> Signup and view all the answers

YOLO is a real-time object detection algorithm.

<p>True (A)</p> Signup and view all the answers

What is the key feature of SSD that sets it apart?

<p>It performs object detection and classification in a single forward pass.</p> Signup and view all the answers

What is the U-Net architecture designed for?

<p>Medical image segmentation.</p> Signup and view all the answers

PSPNet handles semantic segmentation with local context information.

<p>False (B)</p> Signup and view all the answers

What type of layers does FCN (Fully Convolutional Networks) predominantly use?

<p>Fully convolutional layers</p> Signup and view all the answers

What are the main advantages of CNNs?

<p>They are good at detecting patterns and features in images, videos, and audio signals, robust to translation, rotation, and scaling invariance, allow for end-to-end training, and can handle large amounts of data.</p> Signup and view all the answers

What are some disadvantages of CNNs?

<p>They are computationally expensive to train, prone to overfitting, require a large amount of labeled data, and can be difficult to interpret.</p> Signup and view all the answers

Flashcards

Multilayer Perceptron (MLP)

A neural network with multiple layers, each composed of interconnected nodes (neurons), where information is processed and transmitted between layers.

Backpropagation

The process of adjusting the weights in a neural network to minimize the difference between the predicted output and the actual output, leading to better performance.

Optimizer

An algorithm that fine-tunes the weights (parameters) of a neural network during training to minimize a chosen loss function. The goal is to improve the model's predictive accuracy.

Gradient Descent

A simple gradient descent algorithm that updates weights based on the gradient of the loss function calculated from the entire training dataset.

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Stochastic Gradient Descent (SGD)

A variation of gradient descent that uses only a single data point at a time to calculate the gradient, making it faster but potentially less stable.

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Mini-batch SGD

A variation of SGD that uses a small batch of data points (instead of a single point or the entire dataset) for gradient calculation.

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SGD with Momentum

An extension of SGD that adds momentum to the weight updates, allowing for faster convergence and overcoming plateaus.

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Adagrad

An adaptive optimizer that adjusts the learning rate for each parameter based on the past gradients, emphasizing parameters with smaller gradients.

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Adadelta

A method that combines aspects of Adagrad and RMSProp, using a moving average of squared gradients for adaptive learning rate control.

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RMSProp

An optimizer that uses exponentially decaying averages of past gradients and squared gradients for adaptive learning rate control.

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Adam

An adaptive optimizer that combines the advantages of momentum and RMSProp, often offering faster convergence and better performance.

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Loss Function

A mathematical function that measures how well a machine learning model's predictions match the actual target values. It acts as a guide for the optimizer to adjust weights.

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Convolutional Neural Network (CNN)

A type of neural network specifically designed to process image data by using convolution operations.

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Convolution Layer

A layer in CNN that performs convolution operations, extracting features from the input image by applying filters to small regions.

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Activation Layer

A layer in CNN that applies a non-linear activation function to the outputs of the convolution layer, introducing non-linearity and allowing the network to learn complex patterns.

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Pooling Layer

A layer in CNN that reduces the spatial dimensions of the feature maps, summarizing information and reducing computational cost.

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Fully Connected Layer

A layer in CNN that takes the outputs of the convolution and pooling layers and connects them to fully connected neurons, similar to a standard neural network.

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Image Representation in CNN

The method by which an image is represented in CNN, where each pixel value is assigned a corresponding numeric value in a multi-dimensional array.

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ReLU (Rectified Linear Unit)

A common activation function that sets negative values to zero and passes positive values through without change. It helps with sparsity and prevents vanishing gradients.

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Sigmoid

A common activation function that compresses input values between 0 and 1, often used in output layers for classification tasks.

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Tanh (Hyperbolic Tangent)

A common activation function that compresses input values between -1 and 1, typically used in hidden layers of neural networks.

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LeNet-5

The initial convolutional neural network developed by Yann LeCun, used for character recognition tasks and influential in early CNN research.

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AlexNet

A type of CNN proposed by Alex Krizhevsky that made significant progress in image recognition due to its depth, use of ReLU activations, dropout, and overlapping max pooling.

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VGGNet

A family of CNN architectures known for using very deep networks with small 3×3 convolutional filters, leading to improved performance in image recognition.

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GoogLeNet (Inception Network)

A type of CNN that uses

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ResNet (Residual Network)

A type of CNN that introduces skip connections or residual blocks to allow information to flow directly from earlier layers to later layers, preventing vanishing gradients.

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DenseNet

A type of CNN that features dense connections between layers, enabling feature reuse and improved gradient flow.

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MobileNet

A type of CNN designed for mobile and embedded devices, using depthwise separable convolutions to reduce computational cost and memory usage.

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EfficientNet

A CNN architecture that optimizes performance by balancing model depth, width, and resolution using a compound scaling approach.

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R-CNN

A family of CNN-based techniques for object detection, starting with R-CNN and evolving into Faster R-CNN and Mask R-CNN.

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YOLO (You Only Look Once)

A real-time object detection system that processes an entire image at once, using a single forward pass.

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SSD (Single Shot Detector)

An object detection architecture that performs both object detection and classification in a single forward pass.

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U-Net

A specialized CNN designed for medical image segmentation, using an encoder-decoder structure with skip connections.

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PSPNet (Pyramid Scene Parsing Network)

A network that handles semantic segmentation using global context information, providing a more comprehensive understanding of the image.

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FCN (Fully Convolutional Networks)

A type of CNN that uses fully convolutional layers to make pixel-wise predictions, suitable for segmentation and other pixel-level tasks.

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Study Notes

Week 14: Building Neural Networks

  • The week focuses on neural networks, specifically multilayer perceptrons and convolutional neural networks (CNNs).
  • Neural networks are complex systems with interconnected nodes that can learn patterns from data.
  • Multilayer perceptrons have layers of interconnected nodes, including input, hidden, and output layers.
  • Weights are assigned to connections between nodes, and backpropagation is used to adjust these weights to optimize the network's output.
  • Backpropagation adjusts weights to minimize differences between predicted and actual values, optimizing the network's output.
  • Optimizers are algorithms to adjust weights during training, minimizing the loss function.
  • Optimizers control how the weights in a neural network are adjusted, crucial for proper functioning. Examples include stochastic gradient descent (SGD) and its variations, Adam, and others.
  • Loss function measures the difference between predicted and actual values, guiding weight adjustments.
  • Various types of loss functions exist, including Mean Squared Error (MSE) and Binary Cross-Entropy.
  • Convolutional neural networks are specialized for image recognition and processing.
  • CNNs process pixel data, using layers like convolution, activation, pooling, and fully connected layers.
  • Layers in a CNN extract features from image data, process the data, and classify it.
  • Convolutional layers apply filters to detect patterns, and activation functions introduce non-linearity.
  • Pooling layers down-sample the image or feature maps, reducing the size while maintaining useful features.
  • Fully connected layers combine the extracted features for classification.
  • CNNs representation of images involves array of pixel values, which can be colored or grayscale.
  • Different CNN architectures like LeNet-5, AlexNet, VGGNet, GoogLeNet, ResNet, DenseNet, and MobileNet, each possessing unique characteristics, are used in various applications.
  • Variants of CNNs perform various tasks, including image classification, object detection, and semantic segmentation.
  • Advantages of CNNs include pattern detection, robustness to transformations, and end-to-end training.
  • Disadvantages include computational costs, potential for overfitting, and limited interpretability.

CNN Layers

  • Convolutional layers: Extract features from the input image using filters.
    • Filters are small matrices that perform operations on portions of the input images.
    • Convolutional operations: Produce activation maps showing features detected by the filters.
    • Kernel size defines the dimensions of the filter.
  • Activation layers: Introduce non-linearity to the network's output.
    • Common activation functions: Rectified Linear Unit (ReLU), Sigmoid, and Tanh.
  • Pooling layers: Reduce the spatial dimensions of the output from previous layers
    • Functions: Down-sampling, reducing the computational load and making the network robust to small transformations in the input image.
    • Pooling types: Max pooling and average pooling.
  • Fully connected layers: Make classifications from features extracted from previous layers.
    • Connect all nodes in a previous layer to all nodes in the current fully connected layer.
    • This layer processes the combined features from the previous layers for classification.

Convolutional Layer Representation

  • Pixel values in an image are represented by numerical values.
  • Pixel values are usually in the form of 0's and 1's.
  • Color images are also represented as a stack of pixel value matrices for red, green, and blue channels.

Optimizers

  • Algorithms used to adjust the weights of a neural network during training.
  • Goal: Minimize the loss function and improve the model's performance.

Loss Functions

  • Mathematical functions used to quantify the difference between the predicted and actual output of a model.
  • Goal: Minimize the loss, improving the model's accuracy. Exemples: Mean Squared Error (MSE), Binary Cross-Entropy.

Other CNN Variants

  • Different CNN variants have been developed for various tasks, and each has its pros and cons.

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Week 14 - COE305 Machine Learning PDF

Description

This week's focus is on building neural networks, emphasizing multilayer perceptrons and convolutional neural networks (CNNs). Learn about the architecture, weight adjustment through backpropagation, and the importance of optimizers and loss functions in training neural networks. Understand how these components work together to improve model performance.

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