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Revision as of 16:49, 11 May 2018
Contents
Overview
Models
Supervised
- Support Vector Machine (SVM)
- Perceptron (P)
- Feed Forward Neural Networks (FF or FFNN)
- Artificial Neural Networks (ANN)
- Deep Neural Networks (DNN)
Convolutional
Sequence
- Recursive Neural (Tensor) Networks (RNTN)
- Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
- Attention Model
- Sequence to Sequence (Seq2Seq)
Unsupervised: Non-Probabilistic
Unsupervised: Probabilistic/Generative
Reinforcement
Hierarchical
More Algorithms
- Hopfield network (HN)
- Markov chains (MC or discrete time Markov Chain, DTMC)
- 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
