Artificial Intelligence Overview
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Which optimizer is primarily used for its ability to adapt the learning rate based on the gradients of weights?

  • Adam (correct)
  • Adagrad
  • SGD
  • Adadelta

    • For regression tasks, which loss function should be used?

  • Mean Absolute Error
  • Categorical Crossentropy
  • Binary Crossentropy
  • Mean Squared Error (correct)

    • Which metric would be most relevant for evaluating the performance of a binary classification model?

  • F1 Score
  • AUC (correct)
  • Precision
  • Recall

    • What is the purpose of using momentum in Stochastic Gradient Descent?

    <p>To accelerate convergence</p> Signup and view all the answers

    Which optimizer is specifically designed to work effectively with sparse gradients?

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

    How many iterations will the model go through if it trains for 10 epochs with 10 batches per epoch?

    <p>100 iterations</p> Signup and view all the answers

    What is the size of each batch if the batch size is set to 32 and there are 2592 samples in the dataset?

    <p>32 samples</p> Signup and view all the answers

    What does the 'history' variable store during the model training?

    <p>Details about training like loss and accuracy</p> Signup and view all the answers

    How is the number of batches per epoch calculated?

    <p>Total samples divided by batch size</p> Signup and view all the answers

    Which optimizer combines momentum with the second moment of the gradient?

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

    What is the purpose of the 'input_dim' parameter in the Dense layer?

    <p>To define the input shape of the data</p> Signup and view all the answers

    If a model is trained with a batch size of 100 and 1000 samples, how many batches will be formed in one epoch?

    <p>10 batches</p> Signup and view all the answers

    What loss function is used in the model compilation step?

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

    What is the role of the parameter 'n' in the context of training neural networks?

    <p>It refers to the number of epochs before stopping.</p> Signup and view all the answers

    Why are BatchNorm layers used in neural networks?

    <p>To speed up convergence and allow larger learning rates.</p> Signup and view all the answers

    What common hyper-parameter affects how the model learns by adjusting the step size during optimization?

    <p>Initial learning rate</p> Signup and view all the answers

    What defines the primary purpose of a Convolutional Neural Network (CNN)?

    <p>Computer vision tasks</p> Signup and view all the answers

    What is the purpose of using k-Fold Cross-Validation?

    <p>To evaluate the model on various splits of the data.</p> Signup and view all the answers

    Which optimizer combines the advantages of both AdaGrad and RMSProp?

    <p>Adam Optimizer</p> Signup and view all the answers

    How is deep learning different from traditional machine learning?

    <p>Deep learning employs multiple layers for learning data representations.</p> Signup and view all the answers

    Which of the following metrics is commonly used for multi-class classification problems?

    <p>Categorical Crossentropy</p> Signup and view all the answers

    In the context of neural networks, what is an epoch?

    <p>One complete pass through the entire dataset</p> Signup and view all the answers

    What is the role of a max pooling layer in a Convolutional Neural Network?

    <p>It reduces the size of the feature maps.</p> Signup and view all the answers

    What type of parameters can affect the training speed and performance of a neural network?

    <p>Learning rate and optimizers</p> Signup and view all the answers

    How does batch normalization impact internal covariate shift?

    <p>It reduces internal covariate shift.</p> Signup and view all the answers

    Which of the following best describes an iteration in the context of training a neural network?

    <p>Updating the model's parameters once</p> Signup and view all the answers

    Which statement is true about the architecture of Convolutional Neural Networks?

    <p>CNNs utilize convolution and pooling layers in their structure.</p> Signup and view all the answers

    What does a batch represent in the context of machine learning?

    <p>A subset of the dataset processed at once</p> Signup and view all the answers

    Which neural network type is most suitable for analyzing time series data?

    <p>Recurrent Neural Network (RNN)</p> Signup and view all the answers

    What does Adam optimizer compute in addition to the weighted average of past gradients?

    <p>Weighted average of past squared gradients</p> Signup and view all the answers

    What characterizes a model that is underfitting?

    <p>High training error and high validation error</p> Signup and view all the answers

    In the context of regularization, what effect does the weight decay coefficient λ have?

    <p>It determines how dominant the regularization is during gradient computation</p> Signup and view all the answers

    Which of the following best describes the dropout method in training a neural network?

    <p>Randomly drop units along with their connections during training</p> Signup and view all the answers

    What is the primary purpose of early stopping during model training?

    <p>To use a validation set to avoid overfitting</p> Signup and view all the answers

    How does l2 weight decay function in the context of regularization?

    <p>It penalizes large weights by adding a term to the loss function</p> Signup and view all the answers

    What is the main goal of mathematical optimization in machine learning?

    <p>To select the best parameters with respect to a given criterion</p> Signup and view all the answers

    What does the cost function J(θ) typically represent in learning as an optimization problem?

    <p>The average of the losses across all training examples plus a regularization term</p> Signup and view all the answers

    Study Notes

    Artificial Intelligence

    • Artificial intelligence (AI) is a broad field encompassing various techniques.
    • Neural networks are used in various AI applications.

    Neural Network

    • Artificial Neural Networks (ANNs) are used for regression and classification.
    • Convolutional Neural Networks (CNNs) are used in computer vision tasks.
    • Recurrent Neural Networks (RNNs) are used for time series analysis.

    ML vs. Deep Learning

    • Deep learning (DL) is a machine learning subfield using multiple layers for learning data representations.
    • DL excels at learning patterns.
    • DL applies a multi-layer process for learning hierarchical features (data representations).
    • In DL, input features (like image pixels) transition through levels transforming into increasingly abstract representations (edges, textures, parts, objects).

    Batches VS epoch VS iterations

    • Batch: A subset of the dataset. The model updates after processing a batch of samples.
    • Epoch: One complete pass through the entire dataset, ensuring each sample is seen exactly once.
    • Iteration: One update of the model's parameters, the number of iterations per epoch is equal to number of batches.

    Training the model

    • train_ds: Training dataset containing features and labels.
    • epochs: Number of times model passes through entire training data.
    • batch_size: Number of samples in each batch for updating model weights.
    • history: Training process details, including loss and accuracy metrics, useful for plotting progress.
    • Given 2592 samples and batch_size of 32, there are 81 batches per epoch.

    Import Libraries

    • Import numpy as np.
    • Import models, layers & optimizers from tensorflow.keras.

    Define a simple model

    • Define a sequential model with specified layers (e.g., Dense layers with activation functions).

    Compile the model

    • Compile the model with specified optimizer, loss function, and metrics.

    Define Parameters

    • Specify parameters like batch_size and epochs.

    Training

    • Train the model using the training data, specified number of epochs, and batch size.

    Adam Optimizer

    • Adam (Adaptive Moment Estimation) combines momentum optimization with second moment of gradient information, improving optimization over Gradient Descent.

    Generalization

    • Underfitting: Model too simple, poorly representing characteristics, leading to high error on both training and validation data.
    • Overfitting: Model too complex, fitting irrelevant noise in data, low training error, but high validation error, thus poor generalization.

    Optimization

    • Mathematical optimization involves selecting the best element from a set based on a certain criterion.
    • Learning is an optimization problem. A cost function (J(0)) calculates the loss and regularization to be minimized.

    Regularization: Weight Decay

    • Adds a regularization term to the loss function, penalizing large weights to prevent overfitting.
    • The weight decay parameter (λ) dictates the strength of regularization during gradient updates.

    Regularization: Dropout

    • Randomly drops units (and their connections) during training.
    • Hyperparameter p controls dropout rate (tuned for optimal performance).
    • Dropout is an ensemble learning method, training multiple 'mini-batch' variations of the network..

    Regularization: Early Stopping

    • Uses a validation set during training to monitor performance.
    • Stops the training process when the validation accuracy (or loss) fails to improve after a set number of epochs (patience).

    Batch Normalization

    • Normalizes input data by calculating the mean and variance within a batch, setting mean to zero and standard deviation to one.
    • Improves training stability and speed.
    • Inserted after convolutional/fully-connected layers and before activation layers.

    Hyper-parameter Tuning

    • Training neural networks often requires tuning several hyperparameters.
    • Common hyperparameters include: Number of layers, neurons per layer, learning rate, batch size, regularization parameters (like L2 penalty and dropout rate).
    • Tuning hyperparameters can be time-consuming depending on network size and complexity.

    k-Fold Cross-Validation

    • Validates a model by dividing the data into k folds, training the model on k-1 folds and testing on remaining one fold. Repeated k times.

    Adam Optimizer

    • An optimizer adjusting model weights based on loss gradients to minimize loss.
    • Combines features of AdaGrad and RMSprop..

    Parameters for Model Compilation

    • Optimizer: Specifies optimization method.
    • Loss: Specifies the loss function.
    • Metrics: Specifies the parameters to evaluate performance.

    Common Options for Each Parameter

    • Lists various possible optimization methods and loss functions, including Adam, SGD, RMSprop, Adagrad, their respective syntax and parameters.

    Hugging Face model

    • Use HuggingFace Hub model with LangChain for using pre-trained models in applications.

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    Lec 5. Artificial Intelligence PDF

    Description

    This quiz covers the fundamentals of Artificial Intelligence, including techniques such as Neural Networks and the distinctions between Machine Learning and Deep Learning. It also explores concepts like batches, epochs, and iterations essential for understanding AI training processes.

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