TensorFlow is a library provided by Google for ML. It is fast and scalable. TensorFlow has a wide variety of tools for visualization and deployment. It has gained popularity in the developer community and has been used in several organizations. In this section, you will see hardware requirements, installation, and a couple of simple examples.
TensorFlow
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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TensorFlow
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