A-Z of Machine Learning and Computer Vision Terms

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Ghost Frames
Ghost Frames
Gradient Descent
Gradient Descent
Greyscale
Greyscale
Ground Truth
Ground Truth
H
H
Hierarchical Clustering
Hierarchical Clustering
Histogram of Oriented Gradients (HOG)
Histogram of Oriented Gradients (HOG)
Human Pose Estimation
Human Pose Estimation
Human in the Loop (HITL)
Human in the Loop (HITL)
Hyperparameter Tuning
Hyperparameter Tuning
Hyperparameters
Hyperparameters
I
I
Image Annotation
Image Annotation
Image Augmentation
Image Augmentation
Image Captioning
Image Captioning
Image Classification
Image Classification
Image Degradation
Image Degradation
Image Generation
Image Generation
Image Processing
Image Processing
Image Recognition
Image Recognition
Image Restoration
Image Restoration
Image Segmentation
Image Segmentation
Imbalanced Data
Imbalanced Data
Imbalanced Dataset
Imbalanced Dataset
In-Context Learning
In-Context Learning
Instance Segmentation
Instance Segmentation
Instance Segmentation
Instance Segmentation
Interpolation
Interpolation
Interpretability
Interpretability
Intersection over Union (IoU)
Intersection over Union (IoU)
J
J
Jaccard Index
Jaccard Index
Jupyter Notebooks
Jupyter Notebooks
K
K
K-Means Clustering
K-Means Clustering
Keypoints
Keypoints
Knowledge Graphs
Knowledge Graphs
L
L
LIDAR
LIDAR
Label
Label
Label Errors
Label Errors
Large Language Model (LLM)
Large Language Model (LLM)
Latent Dirichlet Allocation (LDA)
Latent Dirichlet Allocation (LDA)
Latent Space
Latent Space
Learning Rate
Learning Rate
Linear Discriminant Analysis (LDA)
Linear Discriminant Analysis (LDA)
Linear Regression
Linear Regression
Logistic Regression
Logistic Regression
Long Short-Term Memory (LSTM)
Long Short-Term Memory (LSTM)
Loss Function
Loss Function
M
M
Machine Learning (ML)
Machine Learning (ML)
Manifold Learning
Manifold Learning
Markov Chains
Markov Chains
Mean Average Precision (mAP)
Mean Average Precision (mAP)
Mean Squared Error (MSE)
Mean Squared Error (MSE)
Medical Image Segmentation
Medical Image Segmentation
Micro-Models
Micro-Models
Model Accuracy
Model Accuracy
Model Parameters
Model Parameters
Model Validation
Model Validation
Motion Detection
Motion Detection
Motion Estimation
Motion Estimation
Multi-Task Learning
Multi-Task Learning
N
N
NIfTI
NIfTI
Natural Language Processing (NLP)
Natural Language Processing (NLP)
Neural Architecture Search
Neural Architecture Search
Neural Networks
Neural Networks
Neural Style Transfer
Neural Style Transfer
Noise
Noise
Normalization
Normalization
O
O
Object Detection
Object Detection
Object Localization
Object Localization
Object Recognition
Object Recognition
Object Tracking
Object Tracking
One-Shot Learning
One-Shot Learning
Optical Character Recognition (OCR)
Optical Character Recognition (OCR)
Optimization Algorithms
Optimization Algorithms
Outlier Detection
Outlier Detection
Overfitting
Overfitting
P
P
PACS (Picture Archiving and Communication System)
PACS (Picture Archiving and Communication System)
PR AUC
PR AUC
Pandas and NumPy
Pandas and NumPy
Panoptic Segmentation
Panoptic Segmentation
Parameter-Efficient Fine-Tuning (Prefix-Tuning)
Parameter-Efficient Fine-Tuning (Prefix-Tuning)
Pattern Recognition
Pattern Recognition
Perceptron
Perceptron
Pixel
Pixel
Pool-Based Sampling
Pool-Based Sampling
Pooling
Pooling
Pose Estimation
Pose Estimation
Precision
Precision
Predictive Model Validation
Predictive Model Validation
Principal Component Analysis
Principal Component Analysis
Prompt Chaining
Prompt Chaining
Prompt Engineering
Prompt Engineering
Prompt Injection
Prompt Injection
C

ChatGPT

ChatGPT is a generative artificial intelligence chatbot developed by OpenAI, built on advanced large language model technology​.It was first released in November 2022 and is based on the GPT (Generative Pre-trained Transformer) architecture, specifically fine-tuned for conversational dialogue. ChatGPT is capable of understanding user prompts and producing detailed, contextually appropriate responses in natural language. Under the hood, it leverages a transformer-based neural network with billions of parameters that has been pre-trained on vast amounts of text data and subsequently fine-tuned with human feedback to improve its ability to follow instructions and have interactive conversations​Technically, ChatGPT was trained through a combination of supervised learning and reinforcement learning from human feedback (RLHF)​.In the RLHF stage, human evaluators provided comparisons of model outputs in conversation, which were used to train a reward model; this reward model then guided a policy optimization (using methods like proximal policy optimization) to make the chatbot’s answers more helpful, correct, and aligned with user intentions. As a result, ChatGPT can answer follow-up questions, admit mistakes, challenge incorrect premises, and refuse inappropriate requests in a dialogue, making it significantly more dynamic and safe for interactive use than earlier GPT models. It has been deployed in various applications, from customer support assistants to coding helpers, and its introduction has been credited with accelerating public interest in AI due to its surprisingly human-like fluency​.It’s important to note that while ChatGPT can generate coherent and contextually relevant responses, it doesn’t have true understanding or access to real-time information (unless augmented with external tools), and it may sometimes produce incorrect or nonsensical answers (often termed “hallucinations”). OpenAI continues to refine the model, and as of 2023 ChatGPT’s underlying model has seen upgrades (e.g., GPT-4) that further improve its capabilities.

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