Quantizing the weights of a model reduces its floating-point precision. The weights of the model can perform with a reasonable accuracy metric, despite this reduction in the precision. Modern hardware can perform operations with less precision and with a lower computation time.
Quantizing the weights
by Rajalingappaa Shanmugamani, Rajesh Arumugam
Hands-On Natural Language Processing with Python
- Title Page
- Copyright and Credits
- Packt Upsell
- Foreword
- Contributors
- Preface
- Getting Started
- Basic concepts and terminologies in NLP
- Applications of NLP
- Analyzing sentiment
- Recognizing named entities
- Linking entities
- Translating text
- Natural Language Inference
- Semantic Role Labeling
- Relation extraction
- SQL query generation, or semantic parsing
- Machine Comprehension
- Textual Entailment
- Coreference resolution
- Searching
- Question answering and chatbots
- Converting text-to-voice
- Converting voice-to-text
- Speaker identification
- Spoken dialog systems
- Other applications
- Summary
- Text Classification and POS Tagging Using NLTK
- Deep Learning and TensorFlow
- Semantic Embedding Using Shallow Models
- Text Classification Using LSTM
- Searching and DeDuplicating Using CNNs
- Named Entity Recognition Using Character LSTM
- Text Generation and Summarization Using GRUs
- Question-Answering and Chatbots Using Memory Networks
- The Question-Answering task
- Memory networks for Question-Answering
- Extending memory networks for dialog modeling
- Summary
- Machine Translation Using the Attention-Based Model
- Speech Recognition Using DeepSpeech
- Text-to-Speech Using Tacotron
- Deploying Trained Models
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Quantizing the weights
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