Difference between revisions of "PRIMO.ai"
m (→Convolutional) |
(→Models) |
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==== Convolutional ==== | ==== Convolutional ==== | ||
*[[(Deep) Convolutional Neural Network (DCNN/CNN)]] | *[[(Deep) Convolutional Neural Network (DCNN/CNN)]] | ||
| + | *[[(Deep) Residual Network (DRN) - ResNet]] | ||
| + | ==== Deonvolutional ==== | ||
*[[Deconvolutional Neural Network (DN) / Inverse Graphics Network (IGN)]] | *[[Deconvolutional Neural Network (DN) / Inverse Graphics Network (IGN)]] | ||
| − | |||
| − | |||
==== Sequence ==== | ==== Sequence ==== | ||
*[[Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN)]] | *[[Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN)]] | ||
| Line 27: | Line 27: | ||
*[[Sequence to Sequence (Seq2Seq)]] | *[[Sequence to Sequence (Seq2Seq)]] | ||
*[[(Tree) Recursive Neural (Tensor) Network (RNTN)]] | *[[(Tree) Recursive Neural (Tensor) Network (RNTN)]] | ||
| − | |||
=== Unsupervised: Non-Probabilistic === | === Unsupervised: Non-Probabilistic === | ||
*[[Autoencoder / Encoder-Decoder]] | *[[Autoencoder / Encoder-Decoder]] | ||
*[[(Stacked) Denoising Autoencoder (DAE)]] | *[[(Stacked) Denoising Autoencoder (DAE)]] | ||
*[[Sparse Autoencoder]] | *[[Sparse Autoencoder]] | ||
| − | |||
=== Unsupervised: Probabilistic/Generative === | === Unsupervised: Probabilistic/Generative === | ||
*[[Restricted Boltzmann Machine (RBM)]] | *[[Restricted Boltzmann Machine (RBM)]] | ||
| Line 38: | Line 36: | ||
*[[Variational Autoencoder]] | *[[Variational Autoencoder]] | ||
*[[Generative Adversarial Network (GAN)]] | *[[Generative Adversarial Network (GAN)]] | ||
| − | |||
=== Reinforcement === | === Reinforcement === | ||
* [[Deep Reinforcement Learning]] | * [[Deep Reinforcement Learning]] | ||
Revision as of 21:07, 11 May 2018
Contents
Overview
Models
Supervised
- Support Vector Machine (SVM)
- Perceptron (P)
- Feed Forward Neural Network (FF or FFNN)
- Artificial Neural Network (ANN)
- Deep Neural Network (DNN)
Convolutional
Deonvolutional
Sequence
- Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN)
- Attention Model
- Sequence to Sequence (Seq2Seq)
- (Tree) Recursive Neural (Tensor) Network (RNTN)
Unsupervised: Non-Probabilistic
Unsupervised: Probabilistic/Generative
- Restricted Boltzmann Machine (RBM)
- Deep Belief Network (DBN)
- Variational Autoencoder
- Generative Adversarial Network (GAN)
Reinforcement
Hierarchical
More Algorithms
- Hopfield Network (HN)
- Markov Model (Chain, Discrete Time, Continuous Tme, Hidden)
- Energy-based Model (EBN)
Techniques & Coding
- Data Preprocessing & Feature Exploration
- Activation Functions
- Optimizers
- Pooling
- Hyperparameters
- Visualization
- Transfer Learning
- Competitions
- Repositories
- Python
Frameworks
TensorFlow
Other DL Frameworks
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
Microsoft Azure
Google Cloud AI
Research & Development
- Self Learning Artificial Intelligence
- Explainable Artificial Intelligence
- Differentiable Neural Computer (DNC)
- Capsule Networks (CapNets)
- Generative Agents
- Messaging & Routing
- Deep Distributed Q Network Partial Observability
- Genetic Algorithms
- Natural Language Inference (NLI) and Recognizing Textual Entailment (RTE)
- 3D Simulation Environments
- Connecting Brains
- Other Challenges
