Difference between revisions of "PRIMO.ai"
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*[[Deconvolutional Neural Network (DN) / Inverse Graphics Network (IGN)]] | *[[Deconvolutional Neural Network (DN) / Inverse Graphics Network (IGN)]] | ||
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=== [[Unsupervised]] === | === [[Unsupervised]] === | ||
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- 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 | ||
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*[[Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)]] | *[[Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)]] | ||
*[[Hierarchical Temporal Memory (HTM)]] Time | *[[Hierarchical Temporal Memory (HTM)]] Time | ||
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==== Unsupervised: Non-Probabilistic; e.g. Deterministic ==== | ==== Unsupervised: Non-Probabilistic; e.g. Deterministic ==== | ||
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*[[(Stacked) Denoising Autoencoder (DAE)]] | *[[(Stacked) Denoising Autoencoder (DAE)]] | ||
*[[Sparse Autoencoder (SAE)]] | *[[Sparse Autoencoder (SAE)]] | ||
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== [[Generative]] == | == [[Generative]] == | ||
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* [[Generative Query Network (GQN)]] | * [[Generative Query Network (GQN)]] | ||
* [[Conditional Adversarial Architecture (CAA)]] | * [[Conditional Adversarial Architecture (CAA)]] | ||
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=== Competitive === | === Competitive === | ||
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* [[Kohonen Network (KN)/Self Organizing Maps (SOM)]] | * [[Kohonen Network (KN)/Self Organizing Maps (SOM)]] | ||
| − | + | === [[Semi-Supervised]] === | |
* [[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)]] | ||
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* [[Hierarchical Reinforcement Learning (HRL)]] | * [[Hierarchical Reinforcement Learning (HRL)]] | ||
| − | ==== Time | + | == Sequence == |
| + | |||
| + | * [[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)]] | ||
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| + | === Time === | ||
* [[Temporal Difference (TD) Learning]] | * [[Temporal Difference (TD) Learning]] | ||
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| − | + | = Techniques = | |
| − | + | == Foundation == | |
* [[Math for Intelligence]] | * [[Math for Intelligence]] | ||
* [http://www.arxiv-sanity.com/ Arxiv Sanity Preserver] to accelerate research | * [http://www.arxiv-sanity.com/ Arxiv Sanity Preserver] to accelerate research | ||
| − | + | == Methods == | |
* [[Backpropagation]] | * [[Backpropagation]] | ||
* [[Gradient Boosting Algorithms]] | * [[Gradient Boosting Algorithms]] | ||
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* [[Repositories & Other Algorithms]] | * [[Repositories & Other Algorithms]] | ||
| − | === [[Libraries & Frameworks]] | + | = Implementation = |
| + | == [[Libraries & Frameworks]] == | ||
* [[Libraries & Frameworks Overview]] | * [[Libraries & Frameworks Overview]] | ||
| − | + | === [[TensorFlow]] === | |
* [[TensorFlow Overview & Tutorials]] | * [[TensorFlow Overview & Tutorials]] | ||
* [[TensorBoard]] | * [[TensorBoard]] | ||
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** [[Swift]] | ** [[Swift]] | ||
| − | + | == Tooling == | |
* [[Model Search]] | * [[Model Search]] | ||
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* [[Notebooks; Jupyter and R Markdown]] | * [[Notebooks; Jupyter and R Markdown]] | ||
| − | + | == Coding == | |
* [[Javascript]] | * [[Javascript]] | ||
* [[Python]] | * [[Python]] | ||
* [[Other Coding options]] | * [[Other Coding options]] | ||
| − | + | == [[Platforms: Machine Learning as a Service (MLaaS)]] == | |
* [[Service Capabilities]] | * [[Service Capabilities]] | ||
| − | + | === [[Google Cloud Platform (GCP)]] ...AI with TensorFlow === | |
* [http://www.kubeflow.org/ Kubeflow] ML workflows on Kubernetes | * [http://www.kubeflow.org/ Kubeflow] ML workflows on Kubernetes | ||
** [[Pipelines]] | ** [[Pipelines]] | ||
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* [http://ai.google/education/ Learn from ML experts at Google] | * [http://ai.google/education/ Learn from ML experts at Google] | ||
| − | + | === [[Amazon AWS]] === | |
* [[AWS with TensorFlow]] | * [[AWS with TensorFlow]] | ||
* [[DeepLens - deep learning enabled video camera]] | * [[DeepLens - deep learning enabled video camera]] | ||
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* [http://aws.amazon.com/training/ AWS Training and Certification] | * [http://aws.amazon.com/training/ AWS Training and Certification] | ||
| − | + | === [[Microsoft Azure]] === | |
* [[Azure with TensorFlow]] | * [[Azure with TensorFlow]] | ||
* [[Azure AI Process]] | * [[Azure AI Process]] | ||
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* [http://aischool.microsoft.com/learning-paths AI School] | * [http://aischool.microsoft.com/learning-paths AI School] | ||
| − | + | === [[NVIDIA]] === | |
* [[RAPIDS]] | * [[RAPIDS]] | ||
* [[NVIDIA Deep Learning Institute]] | * [[NVIDIA Deep Learning Institute]] | ||
* [http://on-demand-gtc.gputechconf.com/gtcnew/on-demand-gtc.php?searchByKeyword=&searchItems=&sessionTopic=&sessionEvent=2&sessionYear=2018&sessionFormat=&submit=&select= GTC Sessions] | * [http://on-demand-gtc.gputechconf.com/gtcnew/on-demand-gtc.php?searchByKeyword=&searchItems=&sessionTopic=&sessionEvent=2&sessionYear=2018&sessionFormat=&submit=&select= GTC Sessions] | ||
| − | + | === Kaggle === | |
* [[Kaggle Overview]] | * [[Kaggle Overview]] | ||
* [[Kaggle Kernels]] | * [[Kaggle Kernels]] | ||
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* [http://www.kaggle.com/learn/overview Hands-On Data Science Education] | * [http://www.kaggle.com/learn/overview Hands-On Data Science Education] | ||
| − | + | === Intel === | |
* [[Neural Compute Stick (NCS)]] | * [[Neural Compute Stick (NCS)]] | ||
* [http://software.intel.com/en-us/ai-academy AI Academy] | * [http://software.intel.com/en-us/ai-academy AI Academy] | ||
| − | + | === Apple === | |
* [[Turi]] | * [[Turi]] | ||
Revision as of 17:20, 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
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
- Naive Bayes
- Tree-based...
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
- 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.
Generative
Competitive
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)
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
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
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 & Development
- 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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