PRIMO.ai
On Saturday May 4, 2024 PRIMO.ai has 738 pages
Contents
- 1 Getting Started
- 2 Information Analysis
- 3 Algorithms
- 3.1 Predict values - Regression
- 3.2 Classification ...predict categories
- 3.3 Recommendation
- 3.4 Clustering - Continuous - Dimensional Reduction
- 3.5 Convolutional
- 3.6 Graph
- 3.7 Sequence / Time
- 3.8 Competitive
- 3.9 Semi-Supervised
- 3.10 Natural Language
- 3.11 Reinforcement Learning (RL)
- 3.12 Neuro-Symbolic
- 3.13 Other
- 4 Techniques
- 5 Development & Implementation
Getting Started
Overview
Background
AI Breakthroughs
AI Fun
- Google AI Experiments
- TensorFlow Playground
- TensorFlow.js Demos
- Google AIY Projects Program - Do-it-yourself artificial intelligence
- NVIDIA Playground
- Competitions
How to...
Forward Thinking
Information Analysis
- Framing Context
- Datasets
- Data Preprocessing
- Feature Exploration/Learning
- Batch Norm(alization) & Standardization
- Hyperparameters
- Zero Padding
- Data Augmentation
- Visualization
- Model Assessment:
- Master Data Management (MDM) / Feature Store / Data Lineage / Data Catalog
Algorithms
Predict values - Regression
- Linear Regression
- Ridge Regression
- Lasso Regression
- Elastic Net Regression
- Bayesian Linear Regression
- Logistic Regression (LR)
- Support Vector Regression (SVR)
- Ordinal Regression
- Poisson Regression
- Tree-based...
- General Regression Neural Network (GRNN)
- One-class Support Vector Machine (SVM)
- Gradient Boosting Machine (GBM)
Classification ...predict categories
- Supervised
- Naive Bayes
- K-Nearest Neighbors (KNN)
- Perceptron (P) ...and Multi-layer Perceptron (MLP)
- Feed Forward Neural Network (FF or FFNN)
- Artificial Neural Network (ANN)
- Deep Learning - Deep Neural Network (DNN)
- Kernel Approximation - Kernel Trick
- Logistic Regression (LR)
- Softmax Regression; Multinominal Logistic Regression
- Tree-based...
- Apriori, Frequent Pattern (FP) Growth, Association Rules/Analysis
- Markov Model (Chain, Discrete Time, Continuous Tme, Hidden)
- Unsupervised
Recommendation
Clustering - Continuous - Dimensional Reduction
- Singular Value Decomposition (SVD)
- Principal Component Analysis (PCA)
- K-Means
- Fuzzy C-Means (FCM)
- K-Modes
- Association Rule Learning
- Mean-Shift Clustering
- Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
- Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)
- Restricted Boltzmann Machine (RBM)
- Variational Autoencoder (VAE)
- Biclustering
- Multidimensional Scaling (MDS)
Hierarchical
- Hierarchical Cluster Analysis (HCA)
- Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)
- Hierarchical Temporal Memory (HTM) Time
- Mixture Models; Gaussian
Convolutional
Deconvolutional
Graph
- includes social networks, sensor networks, the entire Internet, 3D Objects (point cloud)
- Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning
- Point Cloud Convolutional Neural Network (PCCNN)
- A hierarchical RNN-based model to predict scene graphs for images
- A multi-granularity reasoning framework for social relation recognition
Sequence / Time
- Sequence to Sequence (Seq2Seq)
- Neural Turing Machine
- Recurrent Neural Network (RNN)
- (Tree) Recursive Neural (Tensor) Network (RNTN)
Time
- Temporal Difference (TD) Learning
- Predict values
Spatialtemporal
Spatial-Temporal Dynamic Network (STDN)
Competitive
- Generative Adversarial Network (GAN)
- Conditional Adversarial Architecture (CAA)
- Kohonen Network (KN)/Self Organizing Maps (SOM)
- Quantum Generative Adversarial Learning (QuGAN - QGAN)
Semi-Supervised
In many practical situations, the cost to label is quite high, since it requires skilled human experts to do that. So, in the absence of labels in the majority of the observations but present in few, semi-supervised algorithms are the best candidates for the model building. These methods exploit the idea that even though the group memberships of the unlabeled data are unknown, this data carries important information about the group parameters.
- Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)
- Context-Conditional Generative Adversarial Network (CC-GAN)
Natural Language
- Natural Language Processing (NLP) involves speech recognition, (speech) translation, understanding (semantic parsing) complete sentences, understanding synonyms of matching words, and sentiment analysis
- Current State of the Art:
- Methods:
- Natural Language Understanding (NLU) or Natural Language Interpretation (NLI)
- Natural Language Generation (NLG) involves writing/generating complete grammatically correct sentences and paragraphs
Reinforcement Learning (RL)
an algorithm receives a delayed reward in the next time step to evaluate its previous action. Therefore based on those decisions, the algorithm will train itself based on the success/error of output. In combination with Neural Networks it is capable of solving more complex tasks.
- Markov Decision Process (MDP)
- Deep Reinforcement Learning (DRL) - DeepRL
- Distributed Deep Reinforcement Learning (DeepRL)
- Deep Q Learning (DQN)
- Neural Coreference
- State-Action-Reward-State-Action (SARSA)
- Deep Deterministic Policy Gradient (DDPG)
- Trust Region Policy Optimization (TRPO)
- Proximal Policy Optimization (PPO)
- Hierarchical Reinforcement Learning (HRL)
Neuro-Symbolic
the “connectionists” seek to construct artificial neural networks, inspired by biology, to learn about the world, while the “symbolists” seek to build intelligent machines by coding in logical rules and representations of the world. Neuro-Symbolic combines the fruits of group.
Other
Techniques
- Math for Intelligence
- Arxiv Sanity Preserver to accelerate research
Methods & Concepts
- Backpropagation
- Overfitting Challenge
- Dimensional Reduction; identification - what influences an observed outcome
- Activation Functions
- Memory
- Memory Networks
- Attention Mechanism/Transformer Model
- Transformer-XL
- Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking
- Optimizers
- Neural Network Pruning
- Repositories & Other Algorithms
- DAWNBench An End-to-End Deep Learning Benchmark and Competition
- Knowledge Graphs
- Quantization
- Causation vs. Correlation
- Object Detection; Faster R-CNN, YOLO, SSD
- Deep Features
- Local Features
Advanced Learning
- Text Transfer Learning
- Image/Video Transfer Learning
- Few Shot Learning
- Transfer Learning a model trained on one task is re-purposed on a second related task
- Ensemble Learning
- Multi-Task Learning (MTL)
- Apprenticeship Learning - Inverse Reinforcement Learning (IRL)
- Imitation Learning
- Simulated Environment Learning
- Lifelong Learning - Catastrophic Forgetting Challenge
Opportunities & Challenges
- Generative Modeling
- Inside Out - Curious Optimistic Reasoning
- Nature
- Connecting Brains
- Architectures
- Integrity Forensics
- Other Challenges in Artificial Intelligence
Development & Implementation
- Building Your Environment
- Pipelines
- Service Capabilities
- AI Marketplace & Toolkit/Model Interoperability
No Coding
- Automated Machine Learning (AML) - AutoML
- Neural Architecture Search (NAS) Algorithm
- Other codeless options, Code Generators, Drag n' Drop
Coding
Libraries & Frameworks
TensorFlow
- TensorBoard
- TensorFlow Playground
- TensorFlow.js Demos
- TensorFlow.js
- TensorFlow Lite
- TensorFlow Serving
- Related...
Tooling
Platforms: Machine Learning as a Service (MLaaS)
Apple
If you get a 502 or 503 error please try the webpage again, as your message is visiting the island which the server is located, perhaps deciding to relax in the Sun before returning. Thank you.