Real-World Natural Language Processing, Video Edition

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

Files Included :
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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