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Artificial Intelligence Wiki
Topics
Accuracy and Loss
Activation Function
AI Chips for Training and Inference
Artifacts
Artificial General Intelligence (AGI)
AUC (Area under the ROC Curve)
Automated Machine Learning (AutoML)
CI/CD for Machine Learning
Comparison of ML Frameworks
Confusion Matrix
Containers
Convergence
Convolutional Neural Network (CNN)
Datasets and Machine Learning
Data Science vs Machine Learning vs Deep Learning
Distributed Training (TensorFlow, MPI, & Horovod)
Generative Adversarial Network (GAN)
Epochs, Batch Size, & Iterations
ETL
Features, Feature Engineering, & Feature Stores
Gradient Boosting
Gradient Descent
Hyperparameter Optimization
Interpretability
Jupyter Notebooks
Kubernetes
Linear Regression
Logistic Regression
Long Short-Term Memory (LSTM)
Machine Learning Operations (MLOps)
Managing Machine Learning Models
ML Showcase
Metrics in Machine Learning
Machine Learning Models Explained
Model Deployment (Inference)
Model Drift & Decay
Model Training
MNIST
Overfitting vs Underfitting
Random Forest
Recurrent Neural Network (RNN)
Reproducibility in Machine Learning
REST and gRPC
Serverless ML: FaaS and Lambda
Synthetic Data
Structured vs Unstructured Data
Supervised, Unsupervised, & Reinforcement Learning
TensorBoard
Tensor Processing Unit (TPU)
Transfer Learning
Weights and Biases
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Gradient Boosting
Source: Hands-On Machine Learning with R
Gradient boosting (derived from the term gradient boosting machines) is a popular supervised machine learning technique for regression and classification problems that aggregates an ensemble of weak individual models to obtain a more accurate final model.
Gradient boosting is a unique ensemble method since it involves identifying the shortcomings of weak models and incrementally or sequentially building a final ensemble model using a loss function that is optimized with gradient descent. Decision trees are typically the weak learners in gradient boosting and consequently, the technique is sometimes referred to as gradient tree boosting.
​XGBoost is a very popular gradient boosting framework that is fast, uses some clever tricks to obtain more accurate results, and is easy to parallelize.
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Features, Feature Engineering, & Feature Stores
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Gradient Descent
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