List of Algorithms in ML/DL and SL&UL.
Sure! Here is a comprehensive list of machine learning and deep learning algorithms:
Machine Learning Algorithms:
1. Linear Regression
2. Logistic Regression
3. Decision Trees
4. Random Forests
5. Gradient Boosting Methods (e.g., XGBoost, LightGBM)
6. Support Vector Machines (SVM)
7. Naive Bayes
8. k-Nearest Neighbors (k-NN)
9. Principal Component Analysis (PCA)
10. k-Means Clustering
11. Hierarchical Clustering
12. Gaussian Mixture Models (GMM)
13. Hidden Markov Models (HMM)
14. Association Rule Learning (e.g., Apriori, FP-Growth)
15. Reinforcement Learning (e.g., Q-Learning, SARSA)
16. Genetic Algorithms
17. Extreme Learning Machines (ELM)
18. Self-Organizing Maps (SOM)
19. Radial Basis Function Networks (RBFN)
20. Gaussian Processes (GP)
21. Adaptive Boosting (AdaBoost)
22. Multi-layer Perceptron (MLP)
23. Support Vector Regression (SVR)
24. Conditional Random Fields (CRF)
25. Isolation Forest
26. Locally Weighted Regression (LWR)
27. Elastic Net
28. Ridge Regression
29. Lasso Regression
30. Non-negative Matrix Factorization (NMF)
Deep Learning Algorithms:
1. Artificial Neural Networks (ANN)
2. Convolutional Neural Networks (CNN)
3. Recurrent Neural Networks (RNN)
4. Long Short-Term Memory (LSTM)
5. Gated Recurrent Unit (GRU)
6. Deep Boltzmann Machines (DBM)
7. Deep Belief Networks (DBN)
8. Autoencoders
9. Variational Autoencoders (VAE)
10. Generative Adversarial Networks (GAN)
11. Adversarial Autoencoders
12. Deep Q-Networks (DQN)
13. Proximal Policy Optimization (PPO)
14. Actor-Critic Models
15. Transformer Models
16. BERT (Bidirectional Encoder Representations from Transformers)
17. GPT (Generative Pre-trained Transformer)
18. AlphaGo (and other game-playing AI models)
19. U-Net (used for image segmentation)
20. Mask R-CNN (used for object detection and instance segmentation)
21. PointNet (used for point cloud classification)
22. WaveNet (used for speech synthesis)
23. DeepSets (used for set-to-set functions)
24. Neural Turing Machines (NTM)
25. Memory Networks
26. Capsule Networks
27. Attention Mechanisms
Please note that this list covers a wide range of machine learning and deep learning algorithms, but it is not exhaustive. The field of machine learning is vast, and new algorithms and variations continue to be developed as research progresses.
Certainly! Here’s a categorized list of machine learning and deep learning algorithms based on supervised learning and unsupervised learning:
Supervised Learning Algorithms:
1. Linear Regression
2. Logistic Regression
3. Decision Trees
4. Random Forests
5. Gradient Boosting Methods (e.g., XGBoost, LightGBM)
6. Support Vector Machines (SVM)
7. Naive Bayes
8. k-Nearest Neighbors (k-NN)
9. Adaptive Boosting (AdaBoost)
10. Multi-layer Perceptron (MLP)
11. Support Vector Regression (SVR)
12. Conditional Random Fields (CRF)
13. Ridge Regression
14. Lasso Regression
15. Elastic Net
16. Gaussian Processes (GP)
17. Extreme Learning Machines (ELM)
18. Locally Weighted Regression (LWR)
19. Non-negative Matrix Factorization (NMF)
Unsupervised Learning Algorithms:
1. Principal Component Analysis (PCA)
2. k-Means Clustering
3. Hierarchical Clustering
4. Gaussian Mixture Models (GMM)
5. Hidden Markov Models (HMM)
6. Association Rule Learning (e.g., Apriori, FP-Growth)
7. Self-Organizing Maps (SOM)
8. Radial Basis Function Networks (RBFN)
9. Isolation Forest
10. Non-negative Matrix Factorization (NMF)
11. Autoencoders
12. Variational Autoencoders (VAE)
13. Generative Adversarial Networks (GAN)
14. Adversarial Autoencoders
15. Deep Boltzmann Machines (DBM)
16. Deep Belief Networks (DBN)
17. Word2Vec (used for word embeddings)
18. t-SNE (used for data visualization)
19. Restricted Boltzmann Machines (RBM)
Please note that some algorithms, such as Neural Networks and Deep Reinforcement Learning, can be used in both supervised and unsupervised learning contexts depending on the specific task and setup.
