Real-World Natural Language Processing, Video Edition

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Free Download Real-World Natural Language Processing, Video Edition
Released 11/2021
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 9h 47m | Size: 1.56 GB
In Real-world Natural Language Processing you will learn how to

Design, develop, and deploy useful NLP applications
Create named entity taggers
Build machine translation systems
Construct language generation systems and chatbots
Use advanced NLP concepts such as attention and transfer learning
Real-world Natural Language Processing teaches you how to create practical NLP applications without getting bogged down in complex language theory and the mathematics of deep learning. In this engaging book, you'll explore the core tools and techniques required to build a huge range of powerful NLP apps, including chatbots, language detectors, and text classifiers.
About the Technology
Training computers to interpret and generate speech and text is a monumental challenge, and the payoff for reducing labor and improving human/computer interaction is huge! Th e field of Natural Language Processing (NLP) is advancing rapidly, with countless new tools and practices. This unique book offers an innovative collection of NLP techniques with applications in machine translation, voice assistants, text generation, and more.
About the Book
Real-world Natural Language Processing shows you how to build the practical NLP applications that are transforming the way humans and computers work together. Guided by clear explanations of each core NLP topic, you'll create many interesting applications including a sentiment analyzer and a chatbot. Along the way, you'll use Python and open source libraries like AllenNLP and HuggingFace Transformers to speed up your development process.
What's Inside
Design, develop, and deploy useful NLP applications
Create named entity taggers
Build machine translation systems
Construct language generation systems and chatbots
About the Reader
For Python programmers. No prior machine learning knowledge assumed.
About the Author
Masato Hagiwara received his computer science PhD from Nagoya University in 2009. He has interned at Google and Microsoft Research, and worked at Duolingo as a Senior Machine Learning Engineer. He now runs his own research and consulting company.



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1.56 GB | 00:23:06 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

Chapter 1 Building NLP applications (27.99 MB)
Chapter 1 How NLP is used (60.4 MB)
Chapter 1 Introduction to natural language processing (66.01 MB)
Chapter 1 Summary (2.9 MB)
Chapter 10 Avoiding overfitting (33.93 MB)
Chapter 10 Best practices in developing NLP applications (24.81 MB)
Chapter 10 Dealing with imbalanced datasets (25.81 MB)
Chapter 10 Hyperparameter tuning (24.56 MB)
Chapter 10 Summary (2.94 MB)
Chapter 10 Tokenization for neural models (25.71 MB)
Chapter 11 Case study Serving and deploying NLP applications (19.13 MB)
Chapter 11 Deploying and serving NLP applications (41.27 MB)
Chapter 11 Deploying your NLP model (37.64 MB)
Chapter 11 Interpreting and visualizing model predictions (13.13 MB)
Chapter 11 Summary (3.34 MB)
Chapter 11 Where to go from here (7.16 MB)
Chapter 2 Deploying your application (9.09 MB)
Chapter 2 Evaluating your classifier (6.26 MB)
Chapter 2 Loss functions and optimization (9.23 MB)
Chapter 2 Neural networks (21.27 MB)
Chapter 2 Summary (2.65 MB)
Chapter 2 Training your own classifier (10.06 MB)
Chapter 2 Using word embeddings (16.13 MB)
Chapter 2 Working with NLP datasets (35.81 MB)
Chapter 2 Your first NLP application (11.97 MB)
Chapter 3 Building blocks of language Characters, words, and phrases (15.45 MB)
Chapter 3 Document-level embeddings (12.92 MB)
Chapter 3 fastText (11.81 MB)
Chapter 3 GloVe (17.91 MB)
Chapter 3 Skip-gram and continuous bag of words (CBOW) (43.2 MB)
Chapter 3 Summary (2.1 MB)
Chapter 3 Tokenization, stemming, and lemmatization (19.63 MB)
Chapter 3 Visualizing embeddings (9.46 MB)
Chapter 3 Word and document embeddings (18.4 MB)
Chapter 4 Accuracy, precision, recall, and F-measure (14.77 MB)
Chapter 4 Building AllenNLP training pipelines (30.96 MB)
Chapter 4 Case study Language detection (27.81 MB)
Chapter 4 Configuring AllenNLP training pipelines (11.84 MB)
Chapter 4 Long short-term memory units (LSTMs) and gated recurrent units (GRUs) (23.93 MB)
Chapter 4 Sentence classification (38.94 MB)
Chapter 4 Summary (2.59 MB)
Chapter 5 Building a part-of-speech tagger (14.88 MB)
Chapter 5 Modeling a language (23.35 MB)
Chapter 5 Multilayer and bidirectional RNNs (16.94 MB)
Chapter 5 Named entity recognition (27.51 MB)
Chapter 5 Sequential labeling and language modeling (21.06 MB)
Chapter 5 Summary (2.6 MB)
Chapter 5 Text generation using RNNs (31.67 MB)
Chapter 6 Building your first translator (37.54 MB)
Chapter 6 Case study Building a chatbot (24.88 MB)
Chapter 6 Evaluating translation systems (23.62 MB)
Chapter 6 How Seq2Seq models work (49.42 MB)
Chapter 6 Machine translation 101 (26.93 MB)
Chapter 6 Sequence-to-sequence models (17.64 MB)
Chapter 6 Summary (2.3 MB)
Chapter 7 Case study Text classification (17.74 MB)
Chapter 7 Convolutional layers (22.35 MB)
Chapter 7 Convolutional neural networks (19.62 MB)
Chapter 7 Pooling layers (10.66 MB)
Chapter 7 Summary (1.97 MB)
Chapter 8 Attention and Transformer (21.32 MB)
Chapter 8 Case study Spell-checker (53.72 MB)
Chapter 8 Sequence-to-sequence with attention (21 MB)
Chapter 8 Summary (2.25 MB)
Chapter 8 Transformer-based language models (33.54 MB)
Chapter 8 Transformer and self-attention (32.12 MB)
Chapter 9 BERT (52.39 MB)
Chapter 9 Case study 1 Sentiment analysis with BERT (35.07 MB)
Chapter 9 Case study 2 Natural language inference with BERT (33 MB)
Chapter 9 Other pretrained language models (39.46 MB)
Chapter 9 Summary (3.2 MB)
Chapter 9 Transfer learning with pretrained language models (28.97 MB)
Part 1 Basics (4.3 MB)
Part 2 Advanced models (2.73 MB)
Part 3 Putting into production (1.81 MB)

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