# PRIMO.ai

On Saturday December 7, 2019 PRIMO.ai has 547 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
- Try GPT-2...Talk to Transformer - completes your text. | Adam D King, Hugging Face and OpenAI
- Competitions

### How to...

- AI Solver for determining possible algorithms for your needs
- Strategy & Tactics for developing applications
- Checklists for ensuring consistency and completeness

### Forward Thinking

# Information Analysis

- Framing Context
- Datasets
- Imbalanced Data
- Data Preprocessing
- Data Augmentation, Data Labeling, and Auto-Tagging
- Feature Exploration/Learning
- Batch Norm(alization) & Standardization
- Hyperparameters
- Zero Padding
- Train, Validate, and Test
- Model Assessment:
- Visualization
- Master Data Management (MDM) / Feature Store / Data Lineage / Data Catalog
- Data Interoperability

# 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 Time, 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
- A hierarchical RNN-based model to predict scene graphs for images
- A multi-granularity reasoning framework for social relation recognition
- Neural Structured Learning (NSL)

## Sequence / Time

- Sequence to Sequence (Seq2Seq)
- End-to-End Speech
- 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:
- Text Preprocessing
- Relating Text
- Part-of-Speech (POS) Tagging
- Chunking - chunks or patterns, e.g. telephone number
- Chinking - unwanted chunk removal
- Named Entity Recognition (NER)
- Relation Extraction
- Neural Coreference

- 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. Policy Gradient (PG) methods are a type of reinforcement learning techniques that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by gradient descent.

- Monte Carlo (MC) Method - Model Free Reinforcement Learning
- Markov Decision Process (MDP)
- Q Learning
- State-Action-Reward-State-Action (SARSA)
- Deep Reinforcement Learning (DRL) DeepRL
- Distributed Deep Reinforcement Learning (DDRL)
- Deep Q Network (DQN)
- Evolutionary Computation / Genetic Algorithms
- Actor Critic
- 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
- Neural Structured Learning (NSL)

### 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

- Model Search
- Model Monitoring
- Notebooks; Jupyter and R Markdown

### Platforms: Machine Learning as a Service (MLaaS)

### ... and other leading organizations

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