Difference between revisions of "PRIMO.ai"
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== Models == | == Models == | ||
*[[Roadmap for Your Journey]] | *[[Roadmap for Your Journey]] | ||
| − | + | === Basis === | |
*[[Support Vector Machine]] | *[[Support Vector Machine]] | ||
*[[Perceptron]] | *[[Perceptron]] | ||
*[[Artificial Neural Networks (ANN)]] | *[[Artificial Neural Networks (ANN)]] | ||
*[[Deep Neural Networks (DNN)]] | *[[Deep Neural Networks (DNN)]] | ||
| − | + | === Supervised === | |
| − | ==== | ||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
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==== Convolutional ==== | ==== Convolutional ==== | ||
*[[Convolutional Neural Networks (CNN)]] | *[[Convolutional Neural Networks (CNN)]] | ||
**[[Residual Neural Networks (ResNet)]] | **[[Residual Neural Networks (ResNet)]] | ||
| − | |||
| − | |||
==== Sequence ==== | ==== Sequence ==== | ||
*[[Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)]] | *[[Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)]] | ||
*[[Attention Model]] | *[[Attention Model]] | ||
*[[Sequence to Sequence (Seq2Seq)]] | *[[Sequence to Sequence (Seq2Seq)]] | ||
| + | === Unsupervised: Non-Probabilistic === | ||
| + | *[[Autoencoder]] | ||
| + | *[[Stacked de-noising autoencoders]] | ||
| + | *[[Sparse Autoencoder]] | ||
| + | === Unsupervised: Probabilistic/Generative === | ||
| + | *[[Restricted Boltzmann Machine (RBM)]] | ||
| + | *[[Variational Autoencoder]] | ||
| + | *[[Generative Adversarial Networks (GANs)]] | ||
=== Reinforcement === | === Reinforcement === | ||
* [[Deep Reinforcement Learning]] | * [[Deep Reinforcement Learning]] | ||
| − | + | === Other Models === | |
| − | |||
*[[Energy-based Model (EBN)]] | *[[Energy-based Model (EBN)]] | ||
*[[Autoencoders / Encoder-Decoders]] | *[[Autoencoders / Encoder-Decoders]] | ||
Revision as of 11:11, 11 May 2018
Contents
Overview
Models
Basis
Supervised
Convolutional
Sequence
- Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
- Attention Model
- Sequence to Sequence (Seq2Seq)
Unsupervised: Non-Probabilistic
Unsupervised: Probabilistic/Generative
Reinforcement
Other Models
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
- Other Challenges
