- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
A
C
D
G
L
M
N
P
R
S
T
X
- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
What is a Vector and How Do They Work?
A vector is a mathematical approach for expressing and organizing data. The vectorization of data, such as word vectorization, is one of the initial phases in the creation of an ML model. In natural language processing, Word2Vec is a well-known ML model.
When represented data is in vector forms, it is simple and effective to employ all tools available in linear algebra for other tasks like model training or data augmentation. Machines are not human-like in their ability to comprehend text or view images. Input must thus be changed or encoded into numbers or a machine-readable format. Inputs like text and pictures are represented as vectors and matrices, which allows machine learning or deep learning models to be trained and deployed.
Types of Vectors in Machine Learning
As a realistic and helpful way to numerically represent objects to help with a range of analyses, vectors are frequently employed in machine learning (ML). These three vector types are listed below:
Feature Vectors
A feature vector is a set of numerical attributes of observable phenomena that are sorted. It serves as an example of input characteristics for a machine learning model that makes predictions. The features of an item are numerical or symbolic properties that are represented mathematically and simply using feature vectors in machine learning. Feature vectors are crucial in many different facets of pattern recognition in machine learning. For processing and statistical analysis, machine learning algorithms often need a numerical representation of the objects.
Thought Vectors
A thought vector is often a vector of 200–300 values that represents a word. In a single column of integers, a word vector depicts a word's context—its meaning in relation to other words. Specifically, a shallow neural network like word2vec is used to embed the word in a vector space. Word2vec learns to produce the word's context through practiced guesses. Consequently, a thought vector is vectorized thinking, and the vector depicts the connections between various thoughts. To produce the context of a thought, a thought vector is trained.
Word Vectors
A word vector is an effort to quantitatively portray a word's meaning. The frequency with which words appear adjacent to one another in the text is calculated by a computer. Word vectors provide a huge improvement and examine links between words, phrases, and documents. A word vector is an effort to quantitatively portray a word's meaning. The frequency with which words appear adjacent to one another in the text is calculated by a computer. Technologies like speech recognition and machine translation are only feasible because of word vectors. Using a word2vec model with H20.ai, convert words (or sequences of words) to vectors, which produces the best results and is simple to use.
Vectors vs. Scalars vs. Matrices
Vectors
Vectors are numerical arrays. The numbers are placed in order, and each individual number can be identified by its index in that order. Vectors are a part of vector spaces. A vector space is defined as the whole collection of all conceivable vectors of a certain length (or dimension). To complete the given machine learning tasks, the first step is to represent the input. To use home price prediction as an example, the first step is to represent the house, which might include its size, number of stories, location, and so on. A single house can be described with these three components.
Scalars
A scalar is only a number. Like any regular value, it exists. A scalar can be represented by any single value from our collection. A single feature is utilized as input from any dataset and is represented as a scalar value.
Matrices
Matrices are a two-dimensional array of integers, each element is identifiable by two numeric values rather than simply one. Feature inputs are recorded as vectors whereas neural network weights are kept as matrices in deep learning. It is possible to handle these computations quickly by rephrasing the issue in terms of linear algebra. By expressing the problem in terms of tensors and employing methods from linear algebra, it is possible to obtain rapid training times on current GPU technology. Geometric processes like rotation, reflection, and transformation can all be encoded using matrices.
Resources
https://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.transform_word2vec.html