▶️ Activation Function
An activation function is a mathematical function applied to the output of a neuron in a neural network. It determines whether the neuron should be activated or not based on its input. Common activation functions include sigmoid, ReLU, and tanh.
▶️ Adversarial Examples
Adversarial examples are input samples that are intentionally modified to deceive machine learning models, including LLMs. These perturbations can be imperceptible to humans but can cause the model to misclassify the input.
▶️ Attention Mechanism
Attention mechanisms are a key component in many LLM architectures. They allow the model to focus on specific parts of the input sequence when making predictions. Attention mechanisms help LLMs capture long-range dependencies and improve their performance on various tasks.
▶️ Autoencoder
An autoencoder is an unsupervised learning algorithm that consists of an encoder and a decoder. It is used to learn efficient data representations by reconstructing the input data from a compressed representation. Autoencoders can be used for dimensionality reduction, denoising, and anomaly detection.
▶️ Backpropagation
Backpropagation is a common algorithm used to train neural networks, including LLMs. It involves computing the gradients of the network's weights with respect to a loss function, allowing the model to adjust its parameters in the direction that minimizes the loss.
▶️ Bias-Variance Tradeoff
The bias-variance tradeoff refers to the relationship between a model's ability to fit training data (low bias) and its ability to generalize to unseen data (low variance). LLMs need to strike a balance between bias and variance to avoid underfitting or overfitting.
▶️ BLEU Score (Bilingual Evaluation Understudy Score)
BLEU is a metric used to evaluate the quality of machine-generated translations. It compares the generated translation to one or more reference translations and computes a score based on the n-gram overlap between them.
▶️ Bootstrapping
Bootstrapping is a technique used in machine learning to generate multiple training datasets by resampling the original dataset with replacement. It allows LLMs to train on different variations of the data, leading to improved model performance and robustness.
▶️ Batch Normalization
Batch normalization is a technique used to normalize the inputs of each layer in a neural network during training. It helps stabilize and speed up the training process by reducing the internal covariate shift, which can improve the performance of LLMs.
▶️ Bagging
Bagging (Bootstrap Aggregating) is an ensemble learning technique that involves training multiple LLMs on different subsets of the training data, created through bootstrapping. The individual models' predictions are then combined to make a final prediction, typically using voting or averaging.
▶️ Bayesian Optimization
Bayesian optimization is a technique used to optimize the hyperparameters of machine learning models, including LLMs. It combines Bayesian inference with a surrogate model to efficiently explore the hyperparameter space and find the best configuration for the model.
▶️ Beam search
Beam search is a decoding algorithm commonly used in sequence generation tasks, such as machine translation or text generation with LLMs. It explores the most promising paths by keeping a fixed number of top-scoring candidates at each step, reducing the search space.
▶️ Bias
In machine learning, bias refers to the systematic error or tendency of a model to consistently predict values that are different from the true values. It can arise from various sources, such as the model's architecture, assumptions, or the training data.
▶️ Bigram
A bigram is a sequence of two consecutive words in a text. In natural language processing (NLP), bigrams are often used to capture local contextual information, as they represent the relationship between adjacent words. LLMs can utilize bigrams to improve their understanding of language.
▶️ Binary Classification
Binary classification is a type of machine learning task where the goal is to classify instances into one of two classes or categories. For example, determining whether an email is spam or not spam. LLMs can be trained to perform binary classification tasks.
▶️ Boosting
Boosting is an ensemble learning technique where multiple weak learners (usually simple models) are combined to create a stronger model. Each weak learner is trained on a subset of the data, with subsequent learners focusing on the instances that were misclassified by previous ones. Gradient Boosting is a popular boosting algorithm.
▶️ Clustering
Clustering is a technique used to group similar instances together based on their characteristics or features. LLMs can be used for clustering tasks by learning representations of instances and applying clustering algorithms to these representations.
▶️ Convolutional Neural Network (CNN)
A convolutional neural network is a type of neural network architecture commonly used for image and video processing tasks. It consists of convolutional layers that apply filters to input data, allowing the model to automatically learn hierarchical representations of the data.
▶️ Cross-Entropy Loss
Cross-entropy loss, or log loss, is a commonly used loss function in classification tasks. It measures the dissimilarity between predicted probabilities and true class labels. LLMs are often trained using cross-entropy loss to optimize their classification performance.
▶️ Data Augmentation
Data augmentation is a technique used to artificially increase the size of a training dataset by applying various transformations to the existing data. It helps LLMs generalize better by exposing them to diverse variations of the input data.
▶️ Deep Learning
Deep learning refers to a subfield of machine learning that focuses on training models with multiple layers (deep neural networks). LLMs are examples of deep learning models, capable of learning complex patterns and representations from large amounts of data.
▶️ Dropout
Dropout is a regularization technique commonly used in neural networks, including LLMs, to prevent overfitting. It randomly disables a fraction of the neurons during training, forcing the network to learn redundant representations and reducing the reliance on specific neurons.
▶️ Data Leakage
Data leakage refers to the situation where information from outside the training set is inadvertently included in the model's training process. It can lead to overestimated performance during training and poor generalization to unseen data. Preventing data leakage is crucial for training reliable LLMs.
▶️ Decision Tree
A decision tree is a type of supervised learning algorithm used for classification and regression tasks. It represents a flowchart-like structure where each internal node represents a feature, each branch represents a decision rule, and each leaf node represents a class label or a numerical value.
▶️ Dimensionality reduction
Dimensionality reduction techniques are used to reduce the number of features or variables in a dataset while preserving important information. LLMs can learn effective representations that capture essential aspects of the data, enabling dimensionality reduction in an unsupervised manner.
▶️ Dynamic Programming
Dynamic programming is a problem-solving technique used to efficiently solve problems by breaking them down into overlapping subproblems and storing the solutions to avoid redundant computations. It is often used in reinforcement learning algorithms to solve Markov decision processes (MDPs).
▶️ Data Preprocessing
Data preprocessing refers to the steps taken to transform and clean raw data before feeding it to an LLM. It includes tasks such as data cleaning, normalization, feature scaling, handling missing values, and encoding categorical variables to ensure the data is in a suitable format for training.
▶️ Early Stopping
Early stopping is a technique used to prevent overfitting during the training of LLMs. It involves monitoring the model's performance on a validation set and stopping the training process when the performance starts to deteriorate, thus selecting the model with the best generalization.
▶️ Encoder-Decoder
An encoder-decoder is a framework used in sequence-to-sequence tasks, such as machine translation or text summarization. The encoder processes the input sequence and learns a representation, which is then decoded by the decoder to generate the output sequence. LLMs can be used as encoders and decoders in this framework.
▶️ Ensemble Learning
Ensemble learning involves combining multiple models, including LLMs, to improve predictive performance. Each model is trained independently, and their predictions are combined, often by voting or averaging, to make a final prediction. Ensemble learning can enhance the robustness and accuracy of LLMs.
▶️ Error Analysis
Error analysis is the process of examining and understanding the errors made by a machine learning model, including LLMs. It involves analyzing misclassified instances, identifying patterns or systematic mistakes, and using this information to improve the model's performance.
▶️ Exploratory Data Analysis (EDA)
Exploratory data analysis is the initial phase of data analysis, where LLM practitioners explore and summarize the main characteristics and patterns present in the dataset. It involves visualizations, statistical summaries, and data transformations to gain insights and identify important features.
▶️ Elastic Net
Elastic Net is a regularized regression method that combines both L1 (Lasso) and L2 (Ridge) penalties. It is used to overcome the limitations of L1 and L2 regularization alone, providing a balance between sparsity and variable selection. Elastic Net can be applied to LLMs for regression tasks.
▶️ Feature Engineering
Feature engineering is the process of transforming raw data into a set of meaningful features that can be used to train machine learning models, including LLMs. It involves selecting, combining, and creating new features to enhance the model's performance and predictive capabilities.
▶️ Fine-Tuning
Fine-tuning refers to the process of further training a pre-trained LLM on a specific task or dataset. By fine-tuning, the model adapts its learned representations to better suit the target task, leading to improved performance compared to training from scratch.
▶️ F1 Score
The F1 score is a metric commonly used to evaluate the performance of binary classification models. It combines precision (the ratio of true positives to predicted positives) and recall (the ratio of true positives to actual positives) into a single value, providing a balanced measure of accuracy.
▶️ Feature Importance
Feature importance refers to the measure of the influence or relevance of each input feature in a machine learning model's predictions. It helps identify the most influential features and can guide feature selection, engineering, or pruning processes in LLMs.
▶️ Feature Extraction
Feature extraction is the process of transforming raw data into a lower-dimensional representation that captures the most relevant information for a specific task. LLMs can perform feature extraction by learning high-level representations from raw data, allowing for improved efficiency and generalization.
▶️ Generative Adversarial Networks (GANs)
GANs are a type of deep learning model that consists of two neural networks: a generator and a discriminator. The generator aims to generate realistic data samples, such as images, while the discriminator tries to distinguish between the generated samples and real ones. LLMs can be used in the generator or discriminator network of GANs.
▶️ Gradient Descent
Gradient descent is an optimization algorithm used to train machine learning models, including LLMs. It iteratively updates the model's parameters by moving in the direction of steepest descent of the loss function, gradually minimizing the error between predictions and true values.
▶️ Generalization
Generalization refers to the ability of a machine learning model, such as an LLM, to perform well on unseen or test data. A model with good generalization can effectively capture underlying patterns in the training data and apply them to make accurate predictions on new, unseen data.
▶️ Grid Search
Grid search is a technique used for hyperparameter optimization in machine learning. It involves specifying a grid of hyperparameter values and exhaustively evaluating the model's performance for each combination of hyperparameters. Grid search helps in finding the optimal hyperparameters for LLMs.
▶️ Gradient Exploding/Vanishing
Gradient exploding or vanishing occurs during training when the gradients of the model's parameters become either too large (exploding) or too small (vanishing). This can hinder the training process of LLMs, affecting their ability to learn effectively. Techniques like gradient clipping can help mitigate these issues.
▶️ Graph Neural Networks (GNNs)
Graph Neural Networks are a type of neural network specifically designed to operate on graph-structured data. They can capture complex relationships between entities in a graph and are well-suited for tasks such as node classification, graph classification, and link prediction.
▶️ GPU (Graphics Processing Unit)
GPUs are specialized hardware devices that are commonly used to accelerate the training and inference of LLMs. They are highly parallel processors that can perform matrix operations efficiently, enabling faster computation and training of deep learning models.
▶️ Hyperparameter
Hyperparameters are the configuration settings of a machine learning model that are set before the training process begins. Examples of hyperparameters for LLMs include learning rate, regularization strength, number of hidden layers, and activation functions. Choosing appropriate hyperparameter values is crucial for achieving optimal model performance.
▶️ Hierarchical Clustering
Hierarchical clustering is a method used to group similar data points into clusters based on their similarities or distances. It creates a hierarchical structure of clusters, often represented as a dendrogram, which can be useful in exploring the structure and relationships within data.
▶️ Hidden Layer
Hidden Layer
In a neural network, including LLMs, a hidden layer is a layer of neurons that sits between the input layer and the output layer. The hidden layer performs transformations and computations on the input data, enabling the network to learn complex representations and patterns.
▶️ High-Dimensional Data
High-Dimensional Data: High-dimensional data refers to data that has a large number of features or dimensions. LLMs are effective in handling high-dimensional data by learning meaningful representations or by employing dimensionality reduction techniques to reduce the complexity and improve model performance.
▶️ Hypothesis Testing
Hypothesis testing is a statistical method used to make inferences about a population based on a sample of data. It involves formulating a null hypothesis and an alternative hypothesis, and using statistical tests to determine whether there is sufficient evidence to accept or reject the null hypothesis. Hypothesis testing is commonly used in evaluating the performance of LLMs.
▶️ Human-in-the-Loop
Human-in-the-loop refers to a process where human input is integrated into the loop of an automated system. In the context of LLMs, human-in-the-loop approaches involve combining the capabilities of LLMs with human expertise to improve the overall performance and decision-making process.
▶️ Imbalanced Data
Imbalanced data refers to a situation where the distribution of classes or labels in a dataset is uneven, with one or more classes being underrepresented compared to others. Handling imbalanced data is important in LLMs to prevent biased or inaccurate predictions and to ensure fair model performance.
▶️ Inference
Inference refers to the process of applying a trained machine learning model, including LLMs, to make predictions or draw conclusions on new, unseen data. During inference, the model utilizes the learned patterns and parameters to generate output based on the input data.
▶️ Information Retrieval
Information retrieval involves the retrieval of relevant information from a collection of data, typically text-based. LLMs can be used in information retrieval tasks, such as document search or question-answering systems, to understand and generate relevant responses based on user queries.
▶️ Inductive Bias
Inductive bias refers to the prior assumptions or biases built into a machine learning model, including LLMs, that guide the learning process and shape the model's behavior. Inductive bias helps the model generalize from the training data to unseen examples and can influence the model's learning capacity and performance.
▶️ Image Classification
Image classification is a computer vision task that involves assigning labels or categories to images. LLMs, such as convolutional neural networks (CNNs), have demonstrated remarkable performance in image classification by learning hierarchical representations and patterns from image data.
▶️ Joint Probability
Joint probability refers to the probability of two or more events occurring simultaneously. In LLMs, joint probability can be used to model the probability distribution of multiple variables or to estimate the likelihood of observing specific combinations of features.
▶️ Jupyter Notebook
Jupyter Notebook is an open-source web application that allows interactive and collaborative development of code, including machine learning code. It provides an environment where LLM practitioners can write and execute code, visualize data, and document their workflows.
▶️ K-Means Clustering
K-Means Clustering: K-means clustering is a popular unsupervised learning algorithm used for partitioning data into k clusters. It aims to minimize the within-cluster sum of squares by iteratively assigning data points to the nearest centroid and updating the centroids based on the assigned points.
▶️ Knowledge Graphs
Knowledge graphs are structured representations of knowledge that capture relationships and entities in a domain. LLMs can be applied to knowledge graphs for tasks such as knowledge graph completion, entity linking, or question-answering, enabling effective reasoning and inference over the graph.
▶️ k-Nearest Neighbors (k-NN)
k-Nearest Neighbors is a simple and intuitive classification algorithm that assigns a test sample to the majority class among its k nearest neighbors in the feature space. LLMs can employ k-NN as a baseline or as part of more complex ensemble models.
▶️ Knowledge Transfer
Knowledge transfer refers to the process of transferring learned knowledge from one domain or task to another. In the context of LLMs, it involves leveraging pre-trained models or representations on a source task or dataset and applying them to improve performance on a target task or dataset with limited labeled data.
▶️ Long Short-Term Memory (LSTM)
Long Short-Term Memory is a type of recurrent neural network (RNN) architecture that is well-suited for processing sequential data. LSTMs are capable of capturing long-term dependencies and have been widely used in natural language processing, speech recognition, and other time series tasks.
▶️ Learning Rate
Learning rate is a hyperparameter that determines the step size at each iteration of the optimization process during model training. It controls the speed at which a model's parameters are updated based on the gradient. Choosing an appropriate learning rate is essential for effective training of LLMs.
▶️ Loss Function
A loss function, also known as an objective function or cost function, quantifies the discrepancy between predicted and true values during model training. LLMs aim to minimize the loss function to improve their performance. Common loss functions include mean squared error, cross-entropy, and KL divergence.
▶️ Jupyter Notebook
Learning Curve: A learning curve is a graphical representation of a model's performance (such as accuracy or loss) as a function of the amount of training data. It helps analyze how model performance improves or plateaus with increasing data size and provides insights into whether additional data would benefit the LLM.
▶️ Model evaluation
Model evaluation is the process of assessing the performance and generalization ability of a machine learning model. LLMs are evaluated using various metrics, such as accuracy, precision, recall, F1-score, or mean squared error, depending on the specific task and data characteristics.
▶️ Model Selection
Model selection refers to the process of choosing the best machine learning model from a set of candidate models. In the context of LLMs, model selection involves comparing different architectures, hyperparameters, or training strategies to identify the model that performs best on the given task and data.
▶️ Missing Data
Missing data refers to the absence or unavailability of values in a dataset. LLMs need to handle missing data appropriately during training and inference to avoid biased results. Techniques such as imputation or dropout can be used to address missing data in LLMs.
▶️ Markov Chain
A Markov chain is a mathematical model that represents a sequence of events where the future state depends only on the current state and is independent of the past states. LLMs can be used to model and predict the future states in a Markov chain, enabling applications in time series analysis, natural language processing, and more.
▶️ Matrix Factorization
Matrix factorization is a technique used to decompose a matrix into lower-rank matrices. In the context of LLMs, matrix factorization methods are commonly employed for collaborative filtering and recommendation systems, where they can learn latent representations of users and items from sparse interaction data.
▶️ Natural Language Processing (NLP)
Natural Language Processing is a subfield of artificial intelligence that focuses on the interaction between computers and human language. LLMs are widely used in NLP tasks, such as language translation, sentiment analysis, text generation, and question-answering.
▶️ Normalization
Normalization is a preprocessing step that scales input data to a standard range to ensure fair comparison and improve convergence during training. LLMs commonly apply normalization techniques, such as min-max scaling or z-score normalization, to bring features to a similar magnitude.
▶️ Nonlinear Activation Function
A nonlinear activation function is applied to the output of a neuron in a neural network to introduce nonlinearity and enable the network to learn complex, nonlinear relationships in the data. Common nonlinear activation functions used in LLMs include ReLU (Rectified Linear Unit), sigmoid, and tanh.
▶️ Normal Distribution
The normal distribution, also known as the Gaussian distribution, is a continuous probability distribution that is symmetric and bell-shaped. LLMs often assume or model data distributions as normal distributions, enabling probabilistic reasoning, sampling, and generation.
▶️ Nearest Neighbor Search
Nearest Neighbor Search is a technique used to find the most similar or nearest data points to a given query point in a dataset. LLMs can employ nearest neighbor search methods, such as k-d trees or approximate nearest neighbor algorithms, to enable efficient similarity-based retrieval or classification.
▶️ One-Hot Encoding
One-Hot Encoding is a technique used to represent categorical variables as binary vectors. Each category is assigned a binary value, with one element set to 1 and the rest set to 0. LLMs can utilize one-hot encoding to process and learn from categorical data.
▶️ Overfitting
Overfitting occurs when a machine learning model performs well on the training data but fails to generalize well on unseen data. LLMs are susceptible to overfitting due to their large capacity. Regularization techniques, such as dropout or weight decay, can help mitigate overfitting in LLMs.
▶️ Optimization
Optimization refers to the process of finding the optimal set of parameters or hyperparameters for a machine learning model. LLMs employ optimization algorithms, such as stochastic gradient descent (SGD) or Adam, to minimize the loss function and improve model performance during training.
▶️ Outlier
An outlier is an observation that significantly deviates from the normal or expected behavior of the data. Outliers can have a detrimental impact on the performance of LLMs, especially in tasks like regression or clustering. Identifying and handling outliers is important for accurate and robust model training.
▶️ Over-Sampling
Over-sampling is a technique used to address class imbalance in a dataset by artificially increasing the number of minority class samples. LLMs can benefit from over-sampling methods, such as SMOTE (Synthetic Minority Over-sampling Technique), to improve the model's ability to capture and generalize the minority class.
▶️ Object Detection
Object detection is a computer vision task that involves identifying and localizing objects within an image or video. LLMs can be utilized for object detection tasks by combining convolutional neural networks (CNNs) with techniques like region proposal networks or anchor-based methods.
▶️ Pre-training
Pre-training is a technique where a model is initially trained on a large dataset or a related task before fine-tuning it on a target task or dataset. LLMs often undergo pre-training on a large corpus of text data to learn general language representations, which can then be fine-tuned for specific downstream tasks.
▶️ Precision
Precision is a metric used to evaluate the performance of a classification model. It measures the proportion of correctly predicted positive instances among the total predicted positive instances. Precision is commonly used in binary classification tasks and can be calculated as true positives divided by the sum of true positives and false positives.
▶️ Principal Component Analysis (PCA)
Principal Component Analysis (PCA): Principal Component Analysis is a dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional representation. PCA identifies the principal components that capture the maximum variance in the data. LLMs can benefit from PCA to reduce input dimensionality and remove redundant features.
▶️ Pruning
Pruning is a technique used to reduce the size and complexity of a machine learning model by removing unnecessary or redundant connections, nodes, or parameters. LLMs can undergo pruning to improve model efficiency, reduce memory requirements, or enhance interpretability without significantly sacrificing performance.
▶️ Precision-Recall Curve
The Precision-Recall curve is a graphical representation that shows the trade-off between precision and recall (or sensitivity) for different classification thresholds. LLMs can use the Precision-Recall curve to evaluate the model's performance across a range of operating points and choose an appropriate threshold for a given task.
▶️ Pooling
Pooling is an operation commonly used in convolutional neural networks (CNNs) to reduce the spatial dimensions of feature maps. Max pooling and average pooling are popular pooling techniques that extract the most salient features or compute the average values within local regions. LLMs can utilize pooling to downsample feature maps and capture important information.
▶️ Quantum Machine Learning
Quantum Machine Learning is an emerging field that explores the intersection of quantum computing and machine learning. It aims to develop algorithms and models that leverage the unique properties of quantum systems to enhance computational power and solve complex machine learning problems more efficiently.
▶️ Recurrent Neural Network (RNN)
A Recurrent Neural Network is a type of neural network architecture designed to process sequential data by maintaining and utilizing hidden state information. LLMs often employ RNNs to capture temporal dependencies and model sequential patterns in tasks such as natural language processing and time series analysis.
▶️ Reinforcement Learning
Reinforcement Learning is a branch of machine learning concerned with training agents to make sequential decisions in an environment to maximize a reward signal. LLMs can be applied in reinforcement learning frameworks to learn optimal policies and value functions in various domains, such as game playing or robotics.
▶️ Regularization
Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function during model training. LLMs often utilize regularization methods like L1 regularization (Lasso) or L2 regularization (Ridge) to control the complexity of the model and encourage sparse or smooth parameter values.
▶️ Regression
Regression is a type of supervised learning task that aims to predict continuous or real-valued output variables based on input features. LLMs can be used for regression tasks, such as predicting house prices or stock market trends, by learning the underlying patterns and relationships in the data.
▶️ Random Forest
Random Forest is an ensemble learning method that combines multiple decision trees to make predictions. LLMs can utilize random forest algorithms to improve predictive accuracy and handle high-dimensional data or complex feature interactions.
▶️ Robotic Process Automation (RPA)
Robotic Process Automation involves the use of software robots or artificial intelligence to automate repetitive and rule-based tasks in business processes. LLMs can be employed in RPA systems to learn and mimic human decision-making and perform tasks such as data extraction, classification, or workflow automation.
▶️ Sentiment Analysis
Sentiment Analysis, also known as opinion mining, is the task of determining the sentiment or emotion expressed in a piece of text. LLMs can be used for sentiment analysis tasks, such as classifying text as positive, negative, or neutral, by learning the underlying sentiment patterns and contextual cues.
▶️ Stochastic Gradient Descent (SGD)
Stochastic Gradient Descent is an optimization algorithm commonly used to train machine learning models. LLMs employ SGD to iteratively update model parameters using small batches of training data, making it computationally efficient and scalable for large datasets.
▶️ Supervised Learning
Supervised Learning is a machine learning approach where the model learns from labeled data, where inputs are paired with corresponding outputs or labels. LLMs can be trained through supervised learning by optimizing a loss function that quantifies the discrepancy between predicted and true outputs.
▶️ Support Vector Machine (SVM)
Support Vector Machines are supervised learning models used for classification and regression tasks. LLMs can utilize SVMs, which find an optimal hyperplane to separate different classes or predict continuous values, making them effective for tasks such as text classification or image recognition.
▶️ Transfer Learning
Transfer Learning is a technique that allows knowledge gained from one task or domain to be transferred and applied to another related task or domain. LLMs can benefit from transfer learning by leveraging pre-trained models on large datasets to improve performance on new, smaller datasets or different tasks.
▶️ Time Series Analysis
Time Series Analysis is a statistical method for analyzing and forecasting data points collected over time. LLMs can be utilized for time series analysis tasks, such as predicting stock prices or weather patterns, by capturing temporal dependencies and learning patterns in sequential data.
▶️ Transformer
Transformer is a neural network architecture that utilizes self-attention mechanisms to capture dependencies between input and output elements. LLMs based on Transformer models have achieved significant advancements in natural language processing tasks, such as machine translation, language generation, and text understanding.
▶️ Tokenization
Tokenization: Tokenization is the process of breaking down text into smaller units called tokens. LLMs rely on tokenization to convert input text into meaningful and manageable units, such as words, subwords, or characters, which can be further processed and analyzed by the model.
▶️ Tree-Based Models
Tree-Based Models, such as decision trees or random forests, are machine learning models that represent decisions or predictions as a tree-like structure. LLMs can utilize tree-based models for various tasks, including classification, regression, or feature selection, by exploiting the hierarchical and interpretable nature of trees.
▶️ Unsupervised Learning
Unsupervised Learning is a type of machine learning where the model learns from unlabeled data without any predefined labels or target outputs. While LLMs often rely on supervised or semi-supervised learning, they can also be used in unsupervised learning tasks, such as clustering or dimensionality reduction, to discover underlying patterns and structures in the data.
▶️ Validation Set
The Validation Set is a subset of data that is used to evaluate the performance of a machine learning model during the training phase. LLMs can utilize a validation set to monitor and tune the model's hyperparameters, assess generalization, and prevent overfitting.
▶️ Vectorization
Vectorization is the process of converting non-numeric data or operations into numerical representations that can be processed by machine learning models. LLMs often require vectorization techniques, such as word embeddings or one-hot encoding, to represent text or categorical data as numerical vectors.
▶️ Variance
Variance is a statistical measure that quantifies the variability or spread of data points around the mean. In the context of machine learning, variance refers to the sensitivity of a model's predictions to variations in the training data. LLMs aim to strike a balance between capturing complex patterns in the data while minimizing variance to ensure generalization.
▶️ Word Embedding
Word Embedding is a technique that represents words or text as dense vector representations in a continuous space. LLMs often employ word embeddings to capture semantic relationships, contextual information, and meaning in textual data, enabling tasks such as language modeling or sentiment analysis.
▶️ Word2Vec
Word2Vec is a popular algorithm for learning word embeddings from large text corpora. LLMs can leverage Word2Vec to generate distributed word representations, where words with similar contexts are represented by similar vectors, facilitating semantic understanding and feature extraction in natural language processing tasks.
▶️ XGBoost
XGBoost is an open-source software library that implements the gradient boosting framework, a powerful machine learning technique. While not directly related to LLMs, XGBoost can be used in combination with LLMs to improve predictive accuracy and handle structured or tabular data in tasks such as regression, classification, and ranking.
▶️ Yield Curve
The Yield Curve is a graphical representation of the relationship between the interest rates and the time to maturity for a set of fixed-income securities. While not directly related to LLMs, yield curves can be used as economic indicators and can be considered as one of the factors in financial time series analysis, which can involve LLMs.
▶️ Zero-Shot Learning
Zero-Shot Learning is a machine learning paradigm where a model is trained to recognize or classify objects or concepts it has never seen before. LLMs can be utilized in zero-shot learning tasks by learning to generalize from seen classes to unseen classes, leveraging semantic information or attributes associated with the classes.