Master course at the University of Zurich (HS18)
This repository contains all exercises (including description and code in the folder code) and the summary of the lecture (including a book summary of Goldberg: Neural Network Methods for Natural Language Processing).
The summary folder contains :
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summary.md (main file with all content written in Markdown)
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summary.html (html file created from Markdown)
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summary.pdf (best format/presentation style)
The papers folder contains:
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papers.md (main file with a summary of all papers presented during the lecture written in Markdown)
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papers.html (html file created from Markdown)
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papers.pdf (best format/presentation style)
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Chapter 3: Learning Basics and Linear Models
- Train, Validation and Test Set
- Linear Models
- Linear Separability
- Loss Functions
- Regularization
- Gradient-based Optimization
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- Activation Functions
- Different Types of NN-Models
- Training NNs
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Representations (Chapters 6,8,9)
- Types of Features
- Sparse Vector Representations
- Encoding Categorical Features
- Combining Dense Vectors
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- Tips & Tricks regarding Embeddings
- Pre-Trained Word Embeddings
- word2vec, an example of a distributed representation algorithm
- Char-based and Sub-Word Embeddings
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Convolutional Neural Networks (CNNs/ConvNets) (Chapter 13)
- CNN Architecture
- 1D Convolution over Text
- Pooling
- Alternative: Feature-Hashing
- Hierarchical Convolutions
- Issues with Deep CNNs
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Recurrent Neural Networks (RNNs) (Chapters 14,15,16)
- The RNN Abstraction
- RNN Training
- Bi-Directional RNN
- Multi-Layer (Stacked) RNNs / deep RNNs
- RNN Applications / Usages
- RNN Architectures
- LSTM (Long Short-Term Memory)
- GRU (Gated Recurrent Unit)
- Other RNN Usages
- Generators
- Conditioned Generation
- Next Step? Attention
Moritz Eck