Difference between revisions of "PRIMO.ai"
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==== Autoencoder ==== | ==== Autoencoder ==== | ||
*[[Restricted Boltzmann Machine (RBM)]] | *[[Restricted Boltzmann Machine (RBM)]] | ||
| − | *[[Stacked Autoencoder | + | *[[Stacked Autoencoder]] |
*[[Stacked Autoencoder with RBM]] | *[[Stacked Autoencoder with RBM]] | ||
| + | *[[Variational Autoencoder]] | ||
| + | |||
==== Convolutional ==== | ==== Convolutional ==== | ||
*[[Convolutional Neural Networks (CNN)]] | *[[Convolutional Neural Networks (CNN)]] | ||
Revision as of 23:08, 10 May 2018
Contents
Overview
Models
Autoencoder
- Restricted Boltzmann Machine (RBM)
- Stacked Autoencoder
- Stacked Autoencoder with RBM
- Variational Autoencoder
Convolutional
Adversarial
Sequence
- Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
- Attention Model
- Sequence to Sequence (Seq2Seq)
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
