PRIMO.ai

On Friday March 27, 2026 PRIMO.ai has 825 pages

Getting Started

Overview

• How do I leverage AI?
• Courses
• Reading Material & Glossary

Background

• What is AI?
• History of AI
• Current State

AI Breakthroughs

• Capabilities
• Case Studies
• AI Patents after 2013

AI Fun

• Google AI Experiments
• TensorFlow Playground
• TensorFlow.js Demos
• Do-it-yourself artificial intelligence | AIY
• Competitions

How to...

• AI Solver
• Strategy & Tactics
• Checklists

Forward Thinking

• Moonshots
• Journey to Singularity
• Creatives

Datasets & Information Analysis

• Datasets
• Batch Norm(alization) & Standardization
• Data Preprocessing & Feature Exploration/Learning
• Hyperparameters
• Data Augmentation
• Visualization
• Master Data Management (MDM) / Feature Store / Data Lineage / Data Catalog

Algorithms

• About Algorithms & Neural Network Models
• Discriminative vs. Generative
• Intersection of Artificial Intelligence and Architecture | Raj Ramesh

Predict values

• Linear Regression
• Ridge Regression
• Bayesian Linear Regression
• Support Vector Regression (SVR)
• Ordinal Regression
• Poisson Regression
• Tree-based...
  • Fast Forest Quantile Regression
  • Decision Forest Regression
• Boosted Decision Tree Regression
• General Regression Neural Network (GRNN)
• One-class Support Vector Machine (SVM)

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 Neural Network (DNN)
  • Kernel Approximation
    • Support Vector Machine (SVM)
  • Logistic Regression (LR)
  • Tree-based...
    • (Boosted) Decision Tree
    • Random Forest (or) Random Decision Forest
    • Decision Jungle
• Unsupervised
  • Radial Basis Function Network (RBFN)

Recommendation

• Alternating Least Squares (ALS)
• Matrix Factorization

Categorical

• Supervised
  • Apriori, Frequent Pattern (FP) Growth, Association Rules/Analysis
  • Markov Model (Chain, Discrete Time, Continuous Tme, Hidden)
• Unsupervised
  • Autoencoder (AE) / Encoder-Decoder
  • (Stacked) Denoising Autoencoder (DAE)
  • Sparse Autoencoder (SAE)

Clustering - Continuous - Dimensional Reduction

• Singular Value Decomposition (SVD)
• Principal Component Analysis (PCA)
• K-Means
• 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)

Hierarchical

• Hierarchical Cluster Analysis (HCA)
• Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)
• Hierarchical Temporal Memory (HTM) Time
• Mixture Models; Gaussian

Convolutional; Image & Object Recognition

• (Deep) Convolutional Neural Network (DCNN/CNN)
• (Deep) Residual Network (DRN) - ResNet
  • ResNet-50

Graph Convolutional Network (GCN)

- includes social networks, sensor networks, the entire Internet, 3D Objects (point cloud)

• Point Cloud Convolutional Neural Network (CNN)

Deconvolutional

• Deconvolutional Neural Network (DN) / Inverse Graphics Network (IGN)

Other

• Hopfield Network (HN)
• Energy-based Model (EBN)
• Generative Query Network (GQN)

Sequence / Time

• Sequence to Sequence (Seq2Seq)
• Neural Turing Machine
• Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)
  • Bidirectional Long Short-Term Memory (BI-LSTM)
  • Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism
  • Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM)
• (Tree) Recursive Neural (Tensor) Network (RNTN)

Time

• Temporal Difference (TD) Learning
• Predict values
  • Time Series Forecasting Methods - Statistical
  • Time Series Forecasting - Deep Learning

Competitive

• Generative Adversarial Network (GAN)
• Conditional Adversarial Architecture (CAA)
• Kohonen Network (KN)/Self Organizing Maps (SOM)

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 Processing (NLP)

Challenges involve Speech recognition, (speech) translation, understanding (semantic parsing) complete sentences, understanding synonyms of matching words, sentiment analysis, and writing/generating complete grammatically correct sentences and paragraphs

• Topic Model/Mapping
• Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM)

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)
• 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)

Techniques

Foundation

• Math for Intelligence
• Arxiv Sanity Preserver to accelerate research

Methods

• Backpropagation
• Gradient Boosting Algorithms
• Overfitting Challenge
  • Regularization
  • Dropout
• Softmax
• Dimensional Reduction Algorithms; what influences an observed outcome
  • Principal Component Analysis (PCA)
  • Kernel Approximation
  • Isomap
  • Local Linear Embedding (LLE)
  • T-Distributed Stochastic Neighbor Embedding (t-SNE)
  • Pooling / Sub-sampling: Max, Mean
  • Zero Padding
• Activation Functions
• Attention Mechanism/Model
• Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking
• Object Detection; Faster R-CNN, YOLO, SSD
• Optimizers
  • Optimization Methods
  • Objective vs. Cost vs. Loss vs. Error Function
  • Gradient Descent Optimization & Challenges
  • Parameter Initialization
  • Choosing the Right Metric for Evaluating Machine Learning Models
  • Approach to Bias and Variances
  • Evaluation Measures - Classification Performance
• Few Shot Learning
• Multitask Learning
• Transfer Learning a model trained on one task is re-purposed on a second related task
  • Text Transfer Learning
  • Image/Video Transfer Learning
• Repositories & Other Algorithms

Development & Implementation

Libraries & Frameworks

• Libraries & Frameworks Overview

TensorFlow

• TensorFlow Overview & Tutorials
• TensorBoard
• TensorFlow.js
• TensorFlow Playground
• TensorFlow Lite
• TensorFlow Serving
• Related...
  • Keras.js
  • TFLearn
  • Swift

Tooling

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

Coding

• Javascript
• Python
• Other Coding options

Platforms: Machine Learning as a Service (MLaaS)

• Service Capabilities

Google Cloud Platform (GCP) ...AI with TensorFlow

• Kubeflow ML workflows on Kubernetes
  • Pipelines
• Colaboratory - Jupyter notebooks
• Google Developers Codelabs
• Dopamine - reinforcement learning algorithms
• Google AI Experiments
• ML Engine
• Prediction API
• Cloud Vision API - drag & drop picture on webpage
• Grow with Google
• Learn from ML experts at Google

Amazon AWS

• AWS with TensorFlow
• DeepLens - deep learning enabled video camera
  • Getting Started & Project: Object Detection
  • More DeepLens Projects
• AWS Internet of Things (IoT)
  • AWS IoT Button
• AmazonML
• Deep Learning (DL) Amazon Machine Image (AMI) - DLAMI
• FloydHub - training and deploying your DL models
• On-Demand AWS Tech Talks
• AWS Training and Certification

Microsoft Azure

• Azure with TensorFlow
• Azure AI Process
• ML Studio
• Cognitive Services
• Bot Framework
• AI School

NVIDIA

• RAPIDS
• NVIDIA Deep Learning Institute
• GTC Sessions

Kaggle

• Kaggle Overview
• Kaggle Kernels
• Kaggle Competitions
  • Passenger Screening
• Hands-On Data Science Education

Intel

• Neural Compute Stick (NCS)
• AI Academy

Apple

• Turi

Research

• Generative Modeling
• Automated Machine Learning (AML) - AutoML
• Explainable Artificial Intelligence (EAI)
• AI Marketplace & Toolkit/Model Interoperability
• Self Learning Artificial Intelligence - AutoML & World Models
  • Inside Out - Curious Optimistic Reasoning
  • Evolutionary Computation / Genetic Algorithms
  • Apprenticeship Learning - Inverse Reinforcement Learning (IRL)
  • Imitation Learning
  • Simulated Environment Learning
  • 3D Simulation Environments
• Connecting Brains
  • Molecular Artificial Intelligence (AI)
  • Neuroscience
• Architectures
  • Deep Distributed Q Network Partial Observability
  • Graph Nets - Graph Neural Networks
  • Capsule Networks (CapNets)
  • Messaging & Routing
  • Pipelines
  • Processing Units - CPU, GPU, APU, TPU, VPU, FPGA, QPU
• Cybersecurity
• Integrity Forensics
• Other Challenges

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