Difference between revisions of "PRIMO.ai"
(→Algorithms) |
(→Supervised) |
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* Other | * Other | ||
| − | *[[Support Vector Machine (SVM)]] | + | **[[Support Vector Machine (SVM)]] |
| − | *[[Hopfield Network (HN)]] | + | **[[Hopfield Network (HN)]] |
| − | *[[Energy-based Model (EBN)]] | + | **[[Energy-based Model (EBN)]] |
| − | *[[Naive Bayes]] | + | **[[Naive Bayes]] |
| − | *[[Markov Model (Chain, Discrete Time, Continuous Tme, Hidden)]] | + | **[[Markov Model (Chain, Discrete Time, Continuous Tme, Hidden)]] |
| − | *[[Perceptron (P)]] | + | **[[Perceptron (P)]] |
| − | *[[Feed Forward Neural Network (FF or FFNN)]] | + | **[[Feed Forward Neural Network (FF or FFNN)]] |
| − | *[[Artificial Neural Network (ANN)]] | + | **[[Artificial Neural Network (ANN)]] |
| − | *[[Deep Neural Network (DNN)]] | + | **[[Deep Neural Network (DNN)]] |
==== Convolutional ==== | ==== Convolutional ==== | ||
*[[(Deep) Convolutional Neural Network (DCNN/CNN)]] | *[[(Deep) Convolutional Neural Network (DCNN/CNN)]] | ||
Revision as of 22:27, 31 May 2018
Contents
Overview
Background
AI Breakthroughs
AI Fun
How to...
Forward Thinking
Algorithms
Supervised
Labeled (desired solution) data is fed into the algorithm
- Predicting Values
- Other
Convolutional
Deonvolutional
Sequence
- Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)
- Attention Model
- Sequence to Sequence (Seq2Seq)
- (Tree) Recursive Neural (Tensor) Network (RNTN)
- Neural Turing Machine
Unsupervised
Some uses of Unsupervised Learning are (1) data compression, (2) classification, (3) clustering, and (4) outlier detection
Unsupervised: Probabilistic/Generative
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
- Predicting Values
- Clustering
Competitive
Unsupervised: Non-Probabilistic
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.
Reinforcement
An 'agent' algorithm receives a delayed reward in the next time step to evaluate its previous action; 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
Frameworks
TensorFlow
- TensorFlow Overview & Tutorials
- TensorBoard
- TensorFlow.js
- TensorFlow Playground
- TensorFlow Lite
- TensorFlow Serving
- Related...
Techniques
Mathematical Background
Datasets & Information Analysis
Algorithms
Bag of Tricks
- Activation Functions
- Optimizers
- Pooling
- Hyperparameters
- Visualization
- Transfer Learning
- Competitions
Coding
Platforms: Machine Learning as a Service (MLaaS)
Amazon AWS
- AWS with TensorFlow
- AmazonML
- Deep Learning Amazon Machine Image (DLAMI)
- DeepLens - deep learning enabled video camera
- AWS Training and Certification
Google Cloud AI
- Google Cloud AI With TensorFlow
- ML Engine
- Prediction API
- Google Developers Codelabs
- Google AI Experiments
- Cloud Vision API - drag & drop picture on webpage
- Learn from ML experts at Google
Kaggle
Microsoft Azure
Research & Development
- Self Learning Artificial Intelligence - AutoML
- Explainable Artificial Intelligence
- Differentiable Neural Computer (DNC)
- Genetic Algorithms
- Natural Language Inference (NLI) and Recognizing Textual Entailment (RTE)
- 3D Simulation Environments
- Connecting Brains
- Architectures
- Other Challenges
