Artificial Intelligence & Machine Learning Quiz

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Activation functions introduce non-linearity into neural networks, allowing them to learn complex patterns. Without activation functions, neural networks would essentially be linear models, regardless of how many layers they have. Common activation functions include ReLU, sigmoid, and tanh, each with different properties that affect the network's learning capabilities.

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The three main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through trial and error with rewards and punishments. Computational learning is not a recognized category of machine learning.

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Tokenization is the process of breaking down text into smaller units called tokens, which can be words, subwords, or characters. This is a fundamental preprocessing step in NLP that prepares text for further analysis. For example, the sentence "I love machine learning" would be tokenized into ["I", "love", "machine", "learning"].

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YOLO (You Only Look Once) is a popular algorithm for real-time object detection in computer vision. It processes images in a single pass, making it extremely fast compared to other methods. Other popular object detection algorithms include R-CNN, Fast R-CNN, Faster R-CNN, and SSD (Single Shot MultiBox Detector).

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The vanishing gradient problem occurs when gradients become extremely small during backpropagation in deep neural networks, making it difficult for the model to learn. This happens particularly with activation functions like sigmoid or tanh that squash values to a small range. Solutions include using ReLU activation functions, batch normalization, and residual connections.

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Dropout is a regularization technique used to prevent overfitting in neural networks. During training, it randomly sets a fraction of neuron activations to zero at each update, which helps prevent complex co-adaptations on training data. Other regularization techniques include L1/L2 regularization, early stopping, and data augmentation.

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CNNs are particularly effective for image processing because they can automatically learn hierarchical features from images. Early layers learn simple features like edges and colors, while deeper layers learn more complex features like shapes and objects. This hierarchical feature learning is made possible by the convolutional and pooling layers in CNNs.

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In reinforcement learning, a policy is the strategy that an agent follows to decide which action to take in a given state. The goal of reinforcement learning is to find an optimal policy that maximizes the cumulative reward over time. Policies can be deterministic (mapping states to specific actions) or stochastic (mapping states to probability distributions over actions).

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K-Means clustering is an unsupervised learning algorithm that groups similar data points together into clusters. Unlike supervised learning algorithms that require labeled data, K-Means works with unlabeled data to find natural groupings. Other unsupervised learning algorithms include hierarchical clustering, DBSCAN, and principal component analysis (PCA).

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The attention mechanism in transformer models allows the model to focus on different parts of the input sequence when producing outputs. This is particularly useful for tasks like machine translation, where the meaning of a word might depend on its context in the sentence. Self-attention, a key component of transformers, allows each position in the sequence to attend to all positions in the previous layer.

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The F1 score is a common evaluation metric for classification tasks, especially when dealing with imbalanced datasets. It is the harmonic mean of precision and recall, providing a single score that balances both metrics. Other common classification metrics include accuracy, precision, recall, ROC-AUC, and confusion matrix.

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Batch normalization normalizes the activations of a layer to have zero mean and unit variance, which helps stabilize and accelerate the training of deep neural networks. It reduces the internal covariate shift, allowing for higher learning rates and faster convergence. Batch normalization also has a slight regularizing effect.

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Generative Adversarial Networks (GANs) are a type of generative model that can generate new data samples similar to the training data. GANs consist of two neural networks: a generator that creates fake data and a discriminator that tries to distinguish between real and fake data. Through adversarial training, the generator learns to produce increasingly realistic data.

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The pooling layer in a CNN reduces the spatial dimensions of the feature maps, which helps decrease the computational complexity and makes the model more robust to small translations in the input. Common types of pooling include max pooling (which takes the maximum value in each region) and average pooling (which takes the average value).

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Imputation is a common technique for handling missing values in a dataset, where missing values are filled with estimated values. Common imputation methods include mean/median/mode imputation, regression imputation, and k-nearest neighbors imputation. The choice of imputation method depends on the nature of the data and the missingness mechanism.

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Backpropagation is an algorithm used to compute the gradients of the loss function with respect to the weights in a neural network. It works by applying the chain rule of calculus to propagate the error backward through the network. These gradients are then used by optimization algorithms like gradient descent to update the weights and minimize the loss.

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Random forest is an ensemble learning method that combines multiple decision trees to improve the predictive performance and control overfitting. Other ensemble methods include bagging, boosting (AdaBoost, Gradient Boosting, XGBoost), and stacking. Ensemble methods work by combining the predictions of multiple base models to produce a more accurate and robust prediction.

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Recurrent neural networks (RNNs) are designed to process sequential data by maintaining a memory of previous inputs. This makes them suitable for tasks like language modeling, speech recognition, and time series prediction. However, standard RNNs suffer from the vanishing gradient problem, which led to the development of more advanced architectures like LSTMs and GRUs.

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Adam (Adaptive Moment Estimation) is a popular optimization algorithm used in deep learning. It combines the advantages of two other extensions of stochastic gradient descent: AdaGrad and RMSProp. Adam computes adaptive learning rates for each parameter and works well in practice for a wide range of deep learning applications.

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The softmax function is used in classification tasks to convert raw scores (logits) into a probability distribution over classes. It takes a vector of real numbers and transforms it into a probability distribution, where each element is between 0 and 1 and all elements sum to 1. This makes it suitable for multi-class classification problems.

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Fine-tuning a pre-trained model on a new dataset is a common transfer learning technique. Transfer learning leverages knowledge gained from solving one problem and applies it to a different but related problem. This is particularly useful when you have limited data for your target task, as you can benefit from the features learned on a large dataset.

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LSTM units were designed to address the vanishing gradient problem in standard RNNs. They use a system of gates (input gate, forget gate, and output gate) to control the flow of information, allowing the network to remember information for long periods. This makes LSTMs effective for tasks requiring long-term dependencies, such as language modeling and speech recognition.

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Recursive Feature Elimination (RFE) is a common technique for feature selection in machine learning. It works by recursively removing the least important features and building a model with the remaining features. Other feature selection techniques include filter methods (e.g., chi-square test, correlation coefficient), wrapper methods (e.g., forward selection, backward elimination), and embedded methods (e.g., LASSO, Ridge regression).

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Word embedding techniques convert words into numerical vectors that capture semantic relationships between words. These dense vector representations allow machine learning models to process text more effectively than traditional methods like one-hot encoding. Popular word embedding techniques include Word2Vec, GloVe, and fastText, which learn vector representations based on word co-occurrence patterns in large text corpora.

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Isolation Forest is an anomaly detection algorithm that works by isolating anomalies instead of profiling normal data points. It builds a forest of random trees, and anomalies are identified as points that have shorter average path lengths in the trees. Other anomaly detection algorithms include Local Outlier Factor (LOF), One-Class SVM, and Autoencoders.

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Residual connections in ResNet architectures allow training of much deeper networks by mitigating the vanishing gradient problem. They work by adding the input of a layer to its output, creating a shortcut or "skip connection." This allows gradients to flow directly through the network, making it easier to train very deep architectures (hundreds or even thousands of layers).

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Mean squared error (MSE) is a common evaluation metric for regression tasks. It measures the average squared difference between the predicted and actual values, giving higher weight to larger errors. Other common regression metrics include mean absolute error (MAE), root mean squared error (RMSE), R-squared (coefficient of determination), and mean absolute percentage error (MAPE).

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Autoencoders are neural networks designed to learn efficient representations (encodings) of input data in an unsupervised manner. They consist of an encoder that compresses the input into a lower-dimensional representation and a decoder that reconstructs the input from this representation. Autoencoders are used for dimensionality reduction, feature learning, and anomaly detection.

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t-SNE (t-Distributed Stochastic Neighbor Embedding) is a dimensionality reduction technique particularly well-suited for visualizing high-dimensional datasets in a low-dimensional space (typically 2D or 3D). It works by minimizing the divergence between two distributions: one that measures pairwise similarities of the input objects and another that measures pairwise similarities of the corresponding low-dimensional points.

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The attention mechanism in sequence-to-sequence models allows the decoder to focus on different parts of the input sequence when generating each output. This is particularly useful for tasks like machine translation, where different parts of the output might depend on different parts of the input. Attention mechanisms have significantly improved the performance of sequence-to-sequence models.

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Graph Convolutional Networks (GCNs) are a type of graph neural network designed to work with graph-structured data. They generalize the operation of convolution from grid-like data (such as images) to graph data. GCNs have been successfully applied to various tasks, including node classification, link prediction, and graph classification. Other types of graph neural networks include Graph Attention Networks (GATs) and GraphSAGE.

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Q-learning is a model-free reinforcement learning algorithm that learns the optimal policy by estimating the value of taking each action in each state. It does this by updating a Q-value function based on the rewards received and the estimated future rewards. Q-learning is off-policy, meaning it can learn the optimal policy while following a different policy for exploration.

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Model-Agnostic Meta-Learning (MAML) is a meta-learning algorithm designed to train models that can quickly adapt to new tasks with only a few examples. It works by optimizing for a model initialization that can be fine-tuned with a small number of gradient steps and minimal data. Meta-learning, or "learning to learn," aims to develop models that can learn new tasks more efficiently.

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BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model that provides contextualized word representations for a wide range of NLP tasks. Unlike previous models that processed text in one direction (left-to-right or right-to-left), BERT processes text in both directions simultaneously, allowing it to better understand context. BERT can be fine-tuned for specific tasks like question answering, sentiment analysis, and named entity recognition.

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Federated learning is an approach where a model is trained across multiple decentralized devices (like smartphones) while keeping the data local. Instead of sending data to a central server, the model is sent to the devices, trained locally, and only the model updates (not the data) are sent back to the server. This approach helps address privacy concerns and reduces the need for data transfer.

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Gated Recurrent Units (GRUs) were designed to address the vanishing gradient problem in standard RNNs with a simpler architecture than LSTMs. GRUs have two gates (reset gate and update gate) compared to LSTMs' three gates (input gate, forget gate, and output gate). Despite their simpler structure, GRUs often perform comparably to LSTMs on many tasks, with the advantage of being computationally more efficient.

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Siamese networks are a type of few-shot learning approach that learn similarity metrics between inputs. They consist of two identical neural networks that share weights and are trained to output similar embeddings for inputs of the same class and different embeddings for inputs of different classes. This allows the model to generalize to new classes with only a few examples by comparing their embeddings to those of known classes.

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StyleGAN is a generative adversarial network architecture designed to generate high-quality images with control over style attributes. It introduces a style-based generator that allows for control over different aspects of the generated image at different resolutions. This enables features like style mixing (combining styles from different images) and fine-grained control over the generated output.

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Masked language modeling, used in BERT, is a self-supervised learning approach where the model is trained to predict masked words in a sentence. Self-supervised learning is a form of unsupervised learning where the data itself provides the supervision signal. Other self-supervised approaches include contrastive learning, predicting rotations of images, and predicting missing parts of images.

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The transformer architecture processes sequential data using only attention mechanisms without recurrence, addressing the limitations of RNNs in handling long-range dependencies. Introduced in the paper "Attention Is All You Need," transformers use self-attention to weigh the importance of different words in the input sequence. This architecture has become the foundation for many state-of-the-art NLP models, including BERT, GPT, and T5.