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Revision as of 19:43, 1 May 2018
Contents
Overview
Models
- Deep Learning Roadmap
- Deep Neural Networks
- Convolutional Neural Networks
- Generative Adversarial Networks (GANs)
- Sequence to Sequence (Seq2Seq)
Techniques & Coding
- Data Preprocessing & Feature Exploration
- Activation Mechanisms
- Backpropagation
- Hyperparameters
- Competitions
- Repositories
- Python
Frameworks
- Frameworks Overview
- Caffe / Caffe2
- Cognitive Toolkit (CNTK)
- gluon
- Keras
- MXNet
- Neon
- theano
- PyTorch
- Other Frameworks
TensorFlow
Platforms: Machine Learning as a Service (MLaaS)
Amazon AWS
Microsoft Azure
Google Cloud AI
Research & Development
- Self Learning
- Explainable Artificial Intelligence
- Capsule Networks (CapNets)
- Messaging & Routing
- Deep Distributed Q Network Partial Observability
- Deep Reinforcement Learning
- Genetic Algorithms
- Natural Language Inference (NLI) and Recognizing Textual Entailment (RTE)