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Accelerated Natural Language Processing 3.4 - Gated Recurrent Units (GRUs)
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Natural Language Processing
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- 1 Accelerated Natural Language Processing 1.1 - Course Introduction
- 2 Accelerated Natural Language Processing 1.2 - Introduction to Machine Learning
- 3 Accelerated Natural Language Processing 1.3 - ML Applications
- 4 Accelerated Natural Language Processing 1.4 - Supervised and Unsupervised Learning
- 5 Accelerated Natural Language Processing 1.5 - Class Imbalance
- 6 Accelerated Natural Language Processing 1.6 - Missing Values
- 7 Accelerated Natural Language Processing 1.7 - Model Evaluation
- 8 Accelerated Natural Language Processing 1.8 - Introduction to NLP
- 9 Accelerated Natural Language Processing 1.9 - Machine Learning and Text
- 10 Accelerated Natural Language Processing 1.10 - Text Preprocessing
- 11 Accelerated Natural Language Processing 1.11 - Text Vectorization
- 12 Accelerated Natural Language Processing 1.12 - K Nearest Neighbors
- 13 Using Jupyter Notebooks on Sagemaker
- 14 Accelerated Natural Language Processing 2.1 - Tree-based Models
- 15 Accelerated Natural Language Processing 2.2 - Regression Models
- 16 Accelerated Natural Language Processing 2.3 - Optimization
- 17 Accelerated Natural Language Processing 2.4 - Regularization
- 18 Accelerated Natural Language Processing 2.5 - Hyperparameter Tuning
- 19 Accelerated Natural Language Processing 3.1 - Neural Networks
- 20 Accelerated Natural Language Processing 3.2 - Word Vectors
- 21 Accelerated Natural Language Processing 3.3 - Recurrent Neural Networks
- 22 Accelerated Natural Language Processing 3.4 - Gated Recurrent Units (GRUs)
- 23 Accelerated Natural Language Processing 3.5 - Long Short Term Memory (LSTM) Networks
- 24 Accelerated Natural Language Processing 3.6 - Transformers
- 25 Accelerated Natural Language Processing 3.7 - Single Headed Attention
- 26 Accelerated Natural Language Processing 3.8 - Multi Headed Attention
- 27 MLU Channel Introduction
