Contents
Books
A
Attention
- Attention Is All You Need (Paper)
B
Byte Pair Encoding (BPE)
- Neural Machine Translation of Rare Words with Subword Units (Paper): Proposed to use BPE in ML. Source code is also provided in GitHub.
- Byte Pair Encoding (blog): Covers step-by-step implementation with examples.
- Byte Pair Encoding (BPE) and Subword Tokenization (blog): Why subword tokenization is important is explained with step by step explanation and implementation.
C
Connectionist temporal classification (CTC)
CTC is used to train deep neural networks in speech recognition, handwriting recognition and other sequence problems.
- Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. (Original paper)
- Sequence Modeling With CTC (blog): A good explanation with respect to speech recognition.
- An Intuitive Explanation of Connectionist Temporal Classification (blog): Discussed from the hand writing recognition view.
Convolutional Neural Networks (CNN)
- Convolutional Neural Networks for Visual Recognition (blog): An in depth discussion on CNN and its operations. Includes both visual and mathematical concepts.
L
LSTM
- Understanding LSTM Networks (blog): A good introduction to RNN and discussion of the problem. Then why LSTM is needed and block by block explanation.
W
Weight Decay
- Weight Decay: basics with implementations (blog): Mathematical explanation with motivation behind weight decay and implementation form scratch.