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AI algorithms. List of top 100 AI algorithms

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  1. References for learning AI algorithms

List of top 100 AI algorithms

Below is a list of AI algorithms discovered in the LinkedIn post.
All algorithms presented on the picture below.
denis-panjuta. Algorithms. List of top 100 algorithms

Retrived data from the picture:

Machine Learning Algorithms (14 algorithms)

  1. Linear Regression
  2. Logistic Regression
  3. Decision Trees
  4. Random Forests
  5. Support Vector Machines (SVM)
  6. Naive Bayes
  7. K-Nearest Neighbors (KNN)
  8. K-Means Clustering
  9. Principal Component Analysis (PCA)
  10. Gradient Boosting Machines (GBM)
  11. Adaboost
  12. Bagging
  13. Stochastic Gradient Descent (SGD)
  14. Expectation Maximization (EM)

Natural Language Processing Algorithms (12 algorithms)

  1. Bag of Words (BoW)
  2. Term Frequency–Inverse Document Frequency (TF-IDF)
  3. Word2Vec
  4. GloVe (Global Vectors for Word Representation)
  5. Embeddings from Language Models (ELMo)
  6. GPT (Generative Pre-trained Transformer)
  7. Seq2Seq Models
  8. Conditional Random Fields (CRF)
  9. BERT (Bidirectional Encoder Representations from Transformers)
  10. RoBERTa
  11. T5 (Text-to-Text Transformer)
  12. XLNet

Computer Vision Algorithms (13 algorithms)

  1. Edge Detection (Sobel, Prewitt, Canny)
  2. Hough Transform
  3. Scale-Invariant Feature Transform (SIFT)
  4. Histogram of Oriented Gradients (HOG)
  5. Region-Based Convolutional Neural Network (R-CNN)
  6. Fast R-CNN
  7. Mask R-CNN
  8. You Only Look Once (YOLO)
  9. SSD (Single Shot Multibox Detector)
  10. ResNet
  11. Fully Convolutional Networks (FCN)
  12. GAN Variants (Pix2Pix, CycleGAN)
  13. Optical Flow

Reinforcement Learning Algorithms (13 algorithms)

  1. Q-Learning
  2. Deep Q Network (DQN)
  3. Policy Gradients
  4. Actor-Critic Methods
  5. Monte Carlo Tree Search (MCTS)
  6. Proximal Policy Optimization (PPO)
  7. Soft Actor-Critic (SAC)
  8. Trust Region Policy Optimization (TRPO)
  9. SARSA (State-Action-Reward-State-Action)
  10. Double DQN
  11. Dueling DQN
  12. Rainbow DQN
  13. TD-Lambda

Deep Learning Algorithms (14 algorithms)

  1. Convolutional Neural Networks (CNNs)
  2. Recurrent Neural Networks (RNNs)
  3. Long Short-Term Memory (LSTMs)
  4. Generative Adversarial Networks (GANs)
  5. Autoencoders
  6. Deep Belief Networks (DBNs)
  7. Transformers
  8. U-Net
  9. ResNet
  10. VGGNet
  11. InceptionNet
  12. DenseNet
  13. MobileNet
  14. EfficientNet

Optimization Algorithms (14 algorithms)

  1. Gradient Descent
  2. Stochastic Gradient Descent (SGD)
  3. Momentum-Based Methods
  4. Adam (Adaptive Moment Estimation)
  5. RMSprop
  6. Simulated Annealing
  7. Genetic Algorithm
  8. Particle Swarm Optimization (PSO)
  9. Ant Colony Optimization (ACO)
  10. Bayesian Optimization
  11. Nelder–Mead
  12. Powell’s Method
  13. L-BFGS (Limited-memory BFGS)
  14. CMA-ES (Covariance Matrix Adaptation Evolution Strategy)

Ensemble Algorithms (12 algorithms)

  1. Boosting
  2. Bagging
  3. Random Subspace
  4. Stacking
  5. Gradient Boosting Machines (GBM)
  6. XGBoost
  7. CatBoost
  8. LightGBM
  9. Rotation Forest
  10. Voting Classifier
  11. SMOTEBoost
  12. Balanced Random Forest (BRF)

Recommendation Systems (10 algorithms)

  1. Collaborative Filtering
  2. Content-Based Filtering
  3. Matrix Factorization Techniques (SVD, ALS)
  4. Association Rule Learning (Apriori, Eclat)
  5. Neural Collaborative Filtering
  6. Hybrid Recommendation Systems
  7. Context-Aware Recommendation
  8. Reinforcement Learning-Based Recommendation
  9. Deep Learning-Based Recommendation
  10. Knowledge Graph-Based Recommendation

Anomaly Detection Algorithms (9 algorithms)

  1. Isolation Forest
  2. One-Class SVM
  3. Local Outlier Factor (LOF)
  4. Elliptic Envelope
  5. DBSCAN
  6. PCA-Based Anomaly Detection
  7. Autoencoders-Based Anomaly Detection
  8. GMM (Gaussian Mixture Model)
  9. Hawkins–Cramer Test
  10. Graph Algorithms in AI (8 algorithms)**
  11. PageRank
  12. Graph Convolutional Networks (GCNs)
  13. Node2Vec
  14. Graph Attention Networks (GAT)
  15. GraphSAGE
  16. Random Walk Algorithms
  17. Label Propagation Algorithm
  18. Eigenvector-Based Centrality

Table below is showing some algorithms, related information and examples that programmatically shows how it works (this task is in progress and some separated posts will be created later).

Category Algorithm References Examples
Machine Learning Algorithms Linear Regression Algorithm Linear Regression in The Elements of Statistical Learning, Hastie et al., 2009, see on pages 43-50. Example in python code for Linear Regression
Logistic Regression Algorithm Logistic Regression in Pattern Recognition and Machine Learning, Bishop, 2006, see on pages 205-210. Example in python code for Logistic Regression
Decision Trees Algorithm Decision Trees in Classification and Regression Trees, Breiman et al., 1984, see on pages 32-38. Example in python code for Decision Trees
Random Forests Algorithm Random Forests in The Elements of Statistical Learning, Hastie et al., 2009, see on pages 587-606. Example in python code for Random Forests
Support Vector Machines Algorithm SVM in Pattern Recognition and Machine Learning, Bishop, 2006, see on pages 326-337. Example in python code for Support Vector Machines
Deep Learning Algorithms Convolutional Neural Networks (CNNs) Algorithm CNNs in Deep Learning, Goodfellow et al., 2016, see on pages 330-378. Example in python code for CNNs
Recurrent Neural Networks (RNNs) Algorithm RNNs in Deep Learning, Goodfellow et al., 2016, see on pages 372-400. Example in python code for RNNs
Generative Adversarial Networks (GANs) Algorithm GANs in Deep Learning, Goodfellow et al., 2016, see on pages 669-708. Example in python code for GANs
Transformers Algorithm Transformers in Attention Is All You Need, Vaswani et al., 2017. Example in python code for Transformers
Optimization Algorithms Gradient Descent Algorithm Gradient Descent in Convex Optimization, Boyd et al., 2004, see on pages 232-240. Example in python code for Gradient Descent
Stochastic Gradient Descent (SGD) Algorithm SGD in Deep Learning, Goodfellow et al., 2016, see on pages 295-298. Example in python code for SGD
Reinforcement Learning Q-Learning Algorithm Q-Learning in Reinforcement Learning: An Introduction, Sutton & Barto, 1998, see on pages 131-139. Example in python code for Q-Learning
Deep Q-Network (DQN) Algorithm DQN in Playing Atari with Deep Reinforcement Learning, Mnih et al., 2013. Example in python code for DQN
Natural Language Processing BERT (Bidirectional Encoder Representations from Transformers) Algorithm BERT in BERT: Pre-training of Deep Bidirectional Transformers, Devlin et al., 2019. Example in python code for BERT
GPT (Generative Pre-trained Transformer) Algorithm GPT in Improving Language Understanding by Generative Pre-Training, Radford et al., 2018. Example in python code for GPT