838.04 MB | 00:34:26 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English
Files Included :
Chapter 1 Deep learning (38.74 MB)
Chapter 1 Deep learning for NLP (53.1 MB)
Chapter 1 Summary (1.45 MB)
Chapter 1 Vector representations of language (28.49 MB)
Chapter 1 Vector sanitization (10.02 MB)
Chapter 10 Applications of Transformers Hands-on with BERT (8.02 MB)
Chapter 10 Applying BERT (8.09 MB)
Chapter 10 A BERT layer (10.62 MB)
Chapter 10 Fine-tuning BERT (4.01 MB)
Chapter 10 Inspecting BERT (9.5 MB)
Chapter 10 Summary (1.71 MB)
Chapter 10 Training BERT on your data (14.51 MB)
Chapter 2 Deep learning and language The basics (83.96 MB)
Chapter 2 Deep learning and NLP A new paradigm (8.2 MB)
Chapter 2 Summary (1.33 MB)
Chapter 3 From documents to vectors Doc2Vec (23.88 MB)
Chapter 3 From words to vectors Word2Vec (36.62 MB)
Chapter 3 Summary (1.21 MB)
Chapter 3 Text embeddings (33.2 MB)
Chapter 4 Data representation (21.89 MB)
Chapter 4 Models for measuring similarity (33.07 MB)
Chapter 4 Summary (2.66 MB)
Chapter 4 Textual similarity (6.37 MB)
Chapter 4 The data (5.72 MB)
Chapter 5 Data and data processing (13.71 MB)
Chapter 5 Question Answering with sequential models (63.09 MB)
Chapter 5 Sequential NLP (12.97 MB)
Chapter 5 Summary (1.51 MB)
Chapter 6 Data and data processing (14.74 MB)
Chapter 6 Episodic memory for NLP (15.89 MB)
Chapter 6 Semi-supervised memory networks (22.92 MB)
Chapter 6 Strongly supervised memory networks Experiments and results (6.88 MB)
Chapter 6 Summary (1.3 MB)
Chapter 7 Attention (23.01 MB)
Chapter 7 Data (5.17 MB)
Chapter 7 Experiments (15.06 MB)
Chapter 7 Static attention MLP (11.09 MB)
Chapter 7 Summary (1.83 MB)
Chapter 7 Temporal attention LSTM (17.78 MB)
Chapter 8 Multitask learning (10.32 MB)
Chapter 8 Multitask learning for consumer reviews Yelp and Amazon (19.59 MB)
Chapter 8 Multitask learning for part-of-speech tagging and named-entity recognition (13.98 MB)
Chapter 8 Multitask learning for Reuters topic classification (13.32 MB)
Chapter 8 Summary (2.76 MB)
Chapter 9 BERT Masked language modeling (42.08 MB)
Chapter 9 Summary (2.34 MB)
Chapter 9 Transformers (10.8 MB)
Chapter 9 Transformer decoders (11.86 MB)
Chapter 9 Transformer encoders (32.91 MB)
Part 1 Introduction (1.46 MB)
Part 2 Deep NLP (1.25 MB)
Part 3 Advanced topics (1.98 MB)
Chapter 1 Deep learning for NLP (53.1 MB)
Chapter 1 Summary (1.45 MB)
Chapter 1 Vector representations of language (28.49 MB)
Chapter 1 Vector sanitization (10.02 MB)
Chapter 10 Applications of Transformers Hands-on with BERT (8.02 MB)
Chapter 10 Applying BERT (8.09 MB)
Chapter 10 A BERT layer (10.62 MB)
Chapter 10 Fine-tuning BERT (4.01 MB)
Chapter 10 Inspecting BERT (9.5 MB)
Chapter 10 Summary (1.71 MB)
Chapter 10 Training BERT on your data (14.51 MB)
Chapter 2 Deep learning and language The basics (83.96 MB)
Chapter 2 Deep learning and NLP A new paradigm (8.2 MB)
Chapter 2 Summary (1.33 MB)
Chapter 3 From documents to vectors Doc2Vec (23.88 MB)
Chapter 3 From words to vectors Word2Vec (36.62 MB)
Chapter 3 Summary (1.21 MB)
Chapter 3 Text embeddings (33.2 MB)
Chapter 4 Data representation (21.89 MB)
Chapter 4 Models for measuring similarity (33.07 MB)
Chapter 4 Summary (2.66 MB)
Chapter 4 Textual similarity (6.37 MB)
Chapter 4 The data (5.72 MB)
Chapter 5 Data and data processing (13.71 MB)
Chapter 5 Question Answering with sequential models (63.09 MB)
Chapter 5 Sequential NLP (12.97 MB)
Chapter 5 Summary (1.51 MB)
Chapter 6 Data and data processing (14.74 MB)
Chapter 6 Episodic memory for NLP (15.89 MB)
Chapter 6 Semi-supervised memory networks (22.92 MB)
Chapter 6 Strongly supervised memory networks Experiments and results (6.88 MB)
Chapter 6 Summary (1.3 MB)
Chapter 7 Attention (23.01 MB)
Chapter 7 Data (5.17 MB)
Chapter 7 Experiments (15.06 MB)
Chapter 7 Static attention MLP (11.09 MB)
Chapter 7 Summary (1.83 MB)
Chapter 7 Temporal attention LSTM (17.78 MB)
Chapter 8 Multitask learning (10.32 MB)
Chapter 8 Multitask learning for consumer reviews Yelp and Amazon (19.59 MB)
Chapter 8 Multitask learning for part-of-speech tagging and named-entity recognition (13.98 MB)
Chapter 8 Multitask learning for Reuters topic classification (13.32 MB)
Chapter 8 Summary (2.76 MB)
Chapter 9 BERT Masked language modeling (42.08 MB)
Chapter 9 Summary (2.34 MB)
Chapter 9 Transformers (10.8 MB)
Chapter 9 Transformer decoders (11.86 MB)
Chapter 9 Transformer encoders (32.91 MB)
Part 1 Introduction (1.46 MB)
Part 2 Deep NLP (1.25 MB)
Part 3 Advanced topics (1.98 MB)
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