Video description
In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.
"Learn both the theory and practical skills needed to go beyond merely understanding the inner workings of NLP, and start creating your own algorithms or models."
Dr. Arwen Griffioen, Zendesk
Natural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI.
Recent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries—all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before.
Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you’ll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions.
Inside:- Some sentences in this book were written by NLP! Can you guess which ones?
- Working with Keras, TensorFlow, gensim, and scikit-learn
- Rule-based and data-based NLP
- Scalable pipelines
Hobson Lane, Cole Howard, and Hannes Max Hapke are experienced NLP engineers who use these techniques in production.
Provides a great overview of current NLP tools in Python. I’ll definitely be keeping this book on hand for my own NLP work. Highly recommended!
Tony Mullen, Northeastern University–Seattle
An intuitive guide to get you started with NLP. The book is full of programming examples that help you learn in a very pragmatic way.
Tommaso Teofili, Adobe Systems
NARRATED BY MARK THOMAS
Table of contents
- Part 1. Wordy machines
- Chapter 1. Packets of thought (NLP overview)
-
Chapter 2. Build your vocabulary (word tokenization)
- Challenges (a preview of stemming)
- Building your vocabulary with a tokenizer Part 1
- Building your vocabulary with a tokenizer Part 2
- Dot product
- A token improvement
- Extending your vocabulary with n-grams Part 1
- Extending your vocabulary with n-grams Part 2
- Normalizing your vocabulary Part 1
- Normalizing your vocabulary Part 2
- Normalizing your vocabulary Part 3
- Sentiment
- VADER—A rule-based sentiment analyzer
- Chapter 3. Math with words (TF-IDF vectors)
-
Chapter 4. Finding meaning in word counts (semantic analysis)
- From word counts to topic scores
- TF-IDF vectors and lemmatization
- Thought experiment
- An algorithm for scoring topics
- An LDA classifier
- Latent semantic analysis
- Your thought experiment made real
- Singular value decomposition
- U—left singular vectors
- SVD matrix orientation
- Principal component analysis
- Stop horsing around and get back to NLP
- Using truncated SVD for SMS message semantic analysis
- Latent Dirichlet allocation (LDiA)
- LDiA topic model for SMS messages
- Distance and similarity
- Steering with feedback
- Topic vector power
- Semantic search
- Part 2. Deeper learning (neural networks)
- Chapter 5. Baby steps with neural networks (perceptrons and backpropagation)
-
Chapter 6. Reasoning with word vectors (Word2vec)
- Semantic queries and analogies
- Word vectors
- Vector-oriented reasoning
- How to compute Word2vec representations Part 1
- How to compute Word2vec representations Part 2
- How to use the gensim.word2vec module
- How to generate your own word vector representations
- fastText
- Visualizing word relationships
- Unnatural words
- Chapter 7. Getting words in order with convolutional neural networks (CNNs)
- Chapter 8. Loopy (recurrent) neural networks (RNNs)
- Chapter 9. Improving retention with long short-term memory networks
- Chapter 10. Sequence-to-sequence models and attention
- Part 3. Getting real (real-world NLP challenges)
- Chapter 11. Information extraction (named entity extraction and question answering)
- Chapter 12. Getting chatty (dialog engines)
- Chapter 13. Scaling up (optimization, parallelization, and batch processing)
- App A. Your NLP tools
- App B. Playful Python and regular expressions
- App C. Vectors and matrices (linear algebra fundamentals)
- App D. Machine learning tools and techniques
- App F. Locality sensitive hashing
Product information
- Title: Natural Language Processing in Action video edition
- Author(s):
- Release date: April 2019
- Publisher(s): Manning Publications
- ISBN: None
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