Difference between revisions of "PRIMO.ai"
| Line 2: | Line 2: | ||
= Getting Started = | = Getting Started = | ||
| − | == Overview == | + | === Overview === |
* [[How do I leverage AI?]] | * [[How do I leverage AI?]] | ||
* [[Courses]] | * [[Courses]] | ||
* [[Reading Material & Glossary]] | * [[Reading Material & Glossary]] | ||
| − | == Background == | + | === Background === |
* [[What is AI?]] | * [[What is AI?]] | ||
* [[History of AI]] | * [[History of AI]] | ||
* [[Current State]] | * [[Current State]] | ||
| − | == AI Breakthroughs == | + | === AI Breakthroughs === |
* [[Capabilities]] | * [[Capabilities]] | ||
* [[Case Studies]] | * [[Case Studies]] | ||
* [http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=0&p=1&f=S&l=50&Query=%28%28abst%2F%28intelligence+and+%28artificial+or+machine%29%29%29+or+%28aclm%2F%28intelligence+and+%28artificial+or+machine%29%29%29%29+and++%28ISD%2F1%2F1%2F2014-%3E1%2F1%2F2050%29&d=PTXT AI Patents after 2013] | * [http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=0&p=1&f=S&l=50&Query=%28%28abst%2F%28intelligence+and+%28artificial+or+machine%29%29%29+or+%28aclm%2F%28intelligence+and+%28artificial+or+machine%29%29%29%29+and++%28ISD%2F1%2F1%2F2014-%3E1%2F1%2F2050%29&d=PTXT AI Patents after 2013] | ||
| − | == AI Fun == | + | == AI Fun === |
* [http://experiments.withgoogle.com/collection/ai Google AI Experiments] | * [http://experiments.withgoogle.com/collection/ai Google AI Experiments] | ||
* [http://playground.tensorflow.org TensorFlow Playground] | * [http://playground.tensorflow.org TensorFlow Playground] | ||
| Line 24: | Line 24: | ||
* [[Competitions]] | * [[Competitions]] | ||
| − | == How to... == | + | === How to... === |
*[[AI Solver]] | *[[AI Solver]] | ||
*[[Strategy & Tactics]] | *[[Strategy & Tactics]] | ||
*[[Checklists]] | *[[Checklists]] | ||
| − | == Forward Thinking == | + | === Forward Thinking === |
* [[Moonshots]] | * [[Moonshots]] | ||
* [[Journey to Singularity]] | * [[Journey to Singularity]] | ||
* [[Creatives]] | * [[Creatives]] | ||
| + | |||
= Datasets & Information Analysis = | = Datasets & Information Analysis = | ||
| Line 42: | Line 43: | ||
* [[Visualization]] | * [[Visualization]] | ||
* [[Master Data Management (MDM) / Feature Store / Data Lineage / Data Catalog]] | * [[Master Data Management (MDM) / Feature Store / Data Lineage / Data Catalog]] | ||
| + | |||
= Algorithms = | = Algorithms = | ||
| Line 48: | Line 50: | ||
== Discriminative == | == Discriminative == | ||
| + | learn the (hard or soft) boundary between classes; providing classification splits (and not necessarily in a probabilistic manner) | ||
=== [[Supervised]] === | === [[Supervised]] === | ||
- labeled (desired solution) data is fed into the algorithm. The training data set has inputs as well as the desired output. During the training session, the model will adjust its variables to map inputs to the corresponding output. | - labeled (desired solution) data is fed into the algorithm. The training data set has inputs as well as the desired output. During the training session, the model will adjust its variables to map inputs to the corresponding output. | ||
| − | + | ==== ...predict values ==== | |
| − | + | * [[Time Series Forecasting Methods - Statistical]] | |
| − | + | * [[Time Series Forecasting - Deep Learning]] | |
| − | + | * [[Linear 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]] ==== | |
| − | + | * [[Perceptron (P)]] ...and Multi-layer Perceptron (MLP) | |
| − | + | ** [[Feed Forward Neural Network (FF or FFNN)]] | |
| − | + | ** [[Artificial Neural Network (ANN)]] | |
| − | + | ** [[Deep Neural Network (DNN)]] | |
| − | + | * [[Support Vector Machine (SVM)]] | |
| − | + | * [[K-Nearest Neighbors (KNN)]] | |
| − | + | * [[Logistic Regression]] | |
| − | + | * [[Tree-based...]] | |
| − | + | ** [[Boosted Decision Tree]] | |
| − | + | ** [[Random Forest (or) Random Decision Forest]] | |
| − | + | ** [[Decision Jungle]] | |
| − | |||
| − | + | ==== Other ==== | |
| − | + | * [[Hopfield Network (HN)]] | |
| − | + | * [[Energy-based Model (EBN)]] | |
==== Convolutional; Image & Object Recognition ==== | ==== Convolutional; Image & Object Recognition ==== | ||
| − | *[[(Deep) Convolutional Neural Network (DCNN/CNN)]] | + | * [[(Deep) Convolutional Neural Network (DCNN/CNN)]] |
| − | *[[(Deep) Residual Network (DRN) - ResNet]] | + | * [[(Deep) Residual Network (DRN) - ResNet]] |
| − | **[[ResNet-50]] | + | ** [[ResNet-50]] |
==== [[Graph Convolutional Network (GCN)]] ==== | ==== [[Graph Convolutional Network (GCN)]] ==== | ||
| Line 102: | Line 104: | ||
- a probability distribution over a set of classes for each input sample. Unlabeled data is classified as (1) conditional probability of the target Y, or (2) conditional probability of the observable X given a target Y | - a probability distribution over a set of classes for each input sample. Unlabeled data is classified as (1) conditional probability of the target Y, or (2) conditional probability of the observable X given a target Y | ||
| − | + | ==== Classification ==== | |
| − | + | * [[Radial Basis Function Network (RBFN)]] | |
| − | + | ==== Categorical ==== | |
| − | + | * [[Apriori, Frequent Pattern (FP) Growth, Association Rules/Analysis]] | |
| − | + | * [[Markov Model (Chain, Discrete Time, Continuous Tme, Hidden)]] | |
| − | + | ==== [[Clustering]] - Continuous - Dimensional Reduction ==== | |
| − | + | * [[Restricted Boltzmann Machine (RBM)]] | |
| − | + | * [[Variational Autoencoder (VAE)]] | |
| − | + | * [[Singular Value Decomposition (SVD)]] | |
| − | + | * [[Principal Component Analysis (PCA)]] | |
| − | + | * [[K-Means]] | |
| − | + | * [[Mean-Shift Clustering]] | |
| − | + | * [[Density-Based Spatial Clustering of Applications with Noise (DBSCAN)]] | |
| − | + | * [[Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)]] | |
==== [[Hierarchical]] ==== | ==== [[Hierarchical]] ==== | ||
| − | |||
*[[Hierarchical Cluster Analysis (HCA)]] | *[[Hierarchical Cluster Analysis (HCA)]] | ||
*[[Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)]] | *[[Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)]] | ||
| Line 127: | Line 128: | ||
==== Unsupervised: Non-Probabilistic; e.g. Deterministic ==== | ==== Unsupervised: Non-Probabilistic; e.g. Deterministic ==== | ||
- unlabeled data is fed into the algorithm with the algorithm seperating the feature space and return the class associated with the space where a sample originates from. | - unlabeled data is fed into the algorithm with the algorithm seperating the feature space and return the class associated with the space where a sample originates from. | ||
| − | + | * [[Autoencoder (AE) / Encoder-Decoder]] | |
| − | *[[Autoencoder (AE) / Encoder-Decoder]] | + | * [[(Stacked) Denoising Autoencoder (DAE)]] |
| − | *[[(Stacked) Denoising Autoencoder (DAE)]] | + | * [[Sparse Autoencoder (SAE)]] |
| − | *[[Sparse Autoencoder (SAE)]] | ||
=== Sequence === | === Sequence === | ||
| − | |||
* [[Sequence to Sequence (Seq2Seq)]] | * [[Sequence to Sequence (Seq2Seq)]] | ||
* [[Neural Turing Machine]] | * [[Neural Turing Machine]] | ||
| Line 143: | Line 142: | ||
==== Time ==== | ==== Time ==== | ||
| − | |||
* [[Temporal Difference (TD) Learning]] | * [[Temporal Difference (TD) Learning]] | ||
| − | |||
== [[Generative]] == | == [[Generative]] == | ||
| + | model the distribution of individual classes | ||
* [[Generative Query Network (GQN)]] | * [[Generative Query Network (GQN)]] | ||
| − | * [[ | + | |
| + | === Classification [[...predict categories]] === | ||
| + | * [[Naive Bayes]] | ||
=== Competitive === | === Competitive === | ||
* [[Generative Adversarial Network (GAN)]] | * [[Generative Adversarial Network (GAN)]] | ||
| + | * [[Conditional Adversarial Architecture (CAA)]] | ||
* [[Kohonen Network (KN)/Self Organizing Maps (SOM)]] | * [[Kohonen Network (KN)/Self Organizing Maps (SOM)]] | ||
| Line 160: | Line 161: | ||
* [[Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)]] | * [[Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)]] | ||
* [[Context-Conditional Generative Adversarial Network (CC-GAN)]] | * [[Context-Conditional Generative Adversarial Network (CC-GAN)]] | ||
| + | |||
== [[Reinforcement Learning (RL)]] == | == [[Reinforcement Learning (RL)]] == | ||
| Line 173: | Line 175: | ||
* [[Proximal Policy Optimization (PPO)]] | * [[Proximal Policy Optimization (PPO)]] | ||
* [[Hierarchical Reinforcement Learning (HRL)]] | * [[Hierarchical Reinforcement Learning (HRL)]] | ||
| − | |||
| − | |||
| Line 214: | Line 214: | ||
* [[Repositories & Other Algorithms]] | * [[Repositories & Other Algorithms]] | ||
| − | = Implementation = | + | = Development & Implementation = |
== [[Libraries & Frameworks]] == | == [[Libraries & Frameworks]] == | ||
* [[Libraries & Frameworks Overview]] | * [[Libraries & Frameworks Overview]] | ||
| Line 297: | Line 297: | ||
* [[Turi]] | * [[Turi]] | ||
| − | = Research | + | = Research = |
* [[Natural Language Processing (NLP)]] | * [[Natural Language Processing (NLP)]] | ||
* [[Generative]] Modeling | * [[Generative]] Modeling | ||
Revision as of 19:47, 5 January 2019
On Sunday March 29, 2026 PRIMO.ai has 825 pages
Contents
Getting Started
Overview
Background
AI Breakthroughs
AI Fun =
- Google AI Experiments
- TensorFlow Playground
- TensorFlow.js Demos
- Do-it-yourself artificial intelligence | AIY
- Competitions
How to...
Forward Thinking
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
- Intersection of Artificial Intelligence and Architecture | Raj Ramesh
Discriminative
learn the (hard or soft) boundary between classes; providing classification splits (and not necessarily in a probabilistic manner)
Supervised
- labeled (desired solution) data is fed into the algorithm. The training data set has inputs as well as the desired output. During the training session, the model will adjust its variables to map inputs to the corresponding output.
...predict values
- Time Series Forecasting Methods - Statistical
- Time Series Forecasting - Deep Learning
- Linear Regression
- Bayesian Linear Regression
- Support Vector Regression (SVR)
- Ordinal Regression
- Poisson Regression
- Tree-based...
- Boosted Decision Tree Regression
- General Regression Neural Network (GRNN)
- One-class Support Vector Machine (SVM)
Classification ...predict categories
- Perceptron (P) ...and Multi-layer Perceptron (MLP)
- Support Vector Machine (SVM)
- K-Nearest Neighbors (KNN)
- Logistic Regression
- Tree-based...
Other
Convolutional; Image & Object Recognition
Graph Convolutional Network (GCN)
- includes social networks, sensor networks, the entire Internet, 3D Objects (point cloud)
Deconvolutional
Unsupervised
- a probability distribution over a set of classes for each input sample. Unlabeled data is classified as (1) conditional probability of the target Y, or (2) conditional probability of the observable X given a target Y
Classification
Categorical
- Apriori, Frequent Pattern (FP) Growth, Association Rules/Analysis
- Markov Model (Chain, Discrete Time, Continuous Tme, Hidden)
Clustering - Continuous - Dimensional Reduction
- Restricted Boltzmann Machine (RBM)
- Variational Autoencoder (VAE)
- Singular Value Decomposition (SVD)
- Principal Component Analysis (PCA)
- K-Means
- Mean-Shift Clustering
- Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
- Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)
Hierarchical
- Hierarchical Cluster Analysis (HCA)
- Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)
- Hierarchical Temporal Memory (HTM) Time
Unsupervised: Non-Probabilistic; e.g. Deterministic
- unlabeled data is fed into the algorithm with the algorithm seperating the feature space and return the class associated with the space where a sample originates from.
Sequence
- Sequence to Sequence (Seq2Seq)
- Neural Turing Machine
- Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)
- (Tree) Recursive Neural (Tensor) Network (RNTN)
Time
Generative
model the distribution of individual classes
Classification ...predict categories
Competitive
- Generative Adversarial Network (GAN)
- Conditional Adversarial Architecture (CAA)
- Kohonen Network (KN)/Self Organizing Maps (SOM)
Semi-Supervised
- Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)
- Context-Conditional Generative Adversarial Network (CC-GAN)
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
- Softmax
- Dimensional Reduction Algorithms
- Activation Functions
- Attention Mechanism/Model
- Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking
- Object Detection; Faster R-CNN, YOLO, SSD
- Optimizers
- Few Shot Learning
- Multitask Learning
- Transfer Learning a model trained on one task is re-purposed on a second related task
- Recommendation System & Algorithms
- Repositories & Other Algorithms
Development & Implementation
Libraries & Frameworks
TensorFlow
- TensorFlow Overview & Tutorials
- TensorBoard
- TensorFlow.js
- TensorFlow Playground
- TensorFlow Lite
- TensorFlow Serving
- Related...
Tooling
Coding
Platforms: Machine Learning as a Service (MLaaS)
Google Cloud Platform (GCP) ...AI with TensorFlow
- Kubeflow ML workflows on Kubernetes
- 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
- AWS Internet of Things (IoT)
- 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
NVIDIA
Kaggle
Intel
Apple
Research
- Natural Language Processing (NLP)
- Generative Modeling
- Automated Machine Learning (AML) - AutoML
- Explainable Artificial Intelligence (EAI)
- AI Marketplace & Toolkit/Model Interoperability
- Self Learning Artificial Intelligence - AutoML & World Models
- Connecting Brains
- Architectures
- Cybersecurity
- Integrity Forensics
- Other Challenges
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