Difference between revisions of "PRIMO.ai"
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=== Sequence Modeling === | === Sequence Modeling === | ||
*[[Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)]] | *[[Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)]] | ||
| − | |||
**[[Attention Models]] | **[[Attention Models]] | ||
*[[Sequence to Sequence (Seq2Seq)]] | *[[Sequence to Sequence (Seq2Seq)]] | ||
| + | === Other Models === | ||
| + | *[[Restricted Boltzmann Machines]] | ||
| + | *[[Energy-based Model (EBN)]] | ||
| + | *[[Autoencoders / Encoder-Decoders]] | ||
== Techniques & Coding == | == Techniques & Coding == | ||
Revision as of 17:52, 10 May 2018
Contents
Overview
Models
- Deep Learning Roadmap
- Deep Neural Networks (DNN) & Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN)
- Generative Adversarial Networks (GANs)
Sequence Modeling
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
- Deep Reinforcement Learning
- Genetic Algorithms
- Natural Language Inference (NLI) and Recognizing Textual Entailment (RTE)
- 3D Simulation Environments
- Other Challenges
