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Revision as of 09:21, 20 May 2018
Contents
Overview
Background
Breakthroughs in AI
How to...
Forward Thinking
Models
Supervised
- Support Vector Machine (SVM)
- Naive Bayes
- 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), 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: Non-Probabilistic
Unsupervised: Probabilistic/Generative
Competitive
Reinforcement
Hierarchical
More Algorithms
- Hopfield Network (HN)
- Markov Model (Chain, Discrete Time, Continuous Tme, Hidden)
- Energy-based Model (EBN)
Frameworks
TensorFlow
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
Microsoft Azure
Google Cloud AI
Research & Development
- Self Learning Artificial Intelligence - AutoML
- 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
