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All Lessons
Free Lessons (7)
What you'll learn from this course
Overview
Word Embeddings
Introduction
Vocabulary
Embeddings
Skip-gram
Embedding Matrix
Candidate Sampling
Embedding Loss
Cosine Similarity
K-Nearest Neighbors
Quiz
Language Model
Introduction
Language Model
Padding
RNN/LSTM
Dropout
Multiple Layers
LSTM Output
Calculating Loss
Predictions
Tensor Indexing
Quiz
Text Classification
Introduction
Sentiment Analysis
Embeddings
Bidirectional LSTM
Logits
Loss
Classification
Improving the Model
Quiz
Seq2Seq Model
Introduction
Training Data
Final States
Combined State
Encoder-Decoder
Attention
Training Sampler
Decoder Object
Decoding Output
Calculating Loss
Inference Decoding
Model Improvement
Datasets
Quiz
Course Conclusion
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Natural Language Processing with Machine Learning
Introduction
We'll cover the following...
A. Language modeling
B. Language model tasks
In this section, you will be building an LSTM (long short-term memory)
...
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