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Sequence Models (Course 5 of the Deep Learning Specialization)
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https://www.coursera.org/learn/nlp-sequence-models?specialization=deep-learning
시퀀스 모델
deeplearning.ai에서 제공합니다. In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their ... 무료로 등록하십시오.
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Sequence Models (Course 5)
Contents
Week1 Recurrent Neural Networks
- Why Sequence Models?
- Notation
- Recurrent Neural Network Model
- Backpropagation Through Time
- Different Types of RNNs
- Language Model and Sequence Generation
- Sampling Novel Sequences
- Vanishing Gradients with RNNs
- Gated Recurrent Unit (GRU)
- Long Short Term Memory (LSTM)
- Bidirectional RNN
- Deep RNNs
Week2 Natural Language Processing & Word Embeddings
- Word Representation
- Using Word Embeddings
- Properties of Word Embeddings
- Embedding Matrix3m
- Learning Word Embeddings
- Word2Vec
- Negative Sampling
- GloVe Word Vectors
- Sentiment Classification
- Debiasing Word Embeddings
Week3 Sequence Models & Attention Mechanism
- Basic Models
- Picking the Most Likely Sentence
- Beam Search
- Refinements to Beam Search
- Error Analysis in Beam Search
- Bleu Score (Optional)
- Attention Model Intuition
- Attention Model
- Speech Recognition
- Trigger Word Detection
Week4 Transformer Network
- Transformer Network Intuition
- Self-Attention
- Multi-Head Attention
- Transformer Network
- Conclusion and Thank You!
ref.
심층 학습
deeplearning.ai에서 제공합니다. Become a Machine Learning expert. Master the fundamentals of deep learning and break into AI. Recently updated with ... 무료로 등록하십시오.
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https://github.com/amanchadha/coursera-deep-learning-specialization
GitHub - amanchadha/coursera-deep-learning-specialization: Notes, programming assignments and quizzes from all courses within th
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep N...
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