summary of nlp sequence models

Note

This is my personal summary after studying the course, nlp sequence models, which belongs to Deep Learning Specialization. and the copyright belongs to deeplearning.ai.

My personal note

$1_{st}$ week : Building a Recurrent Neural Network Step by Step

• 01_why-sequence-models
• 02_notation
• 03_recurrent-neural-network-model
• 04_backpropagation-through-time
• 05_different-types-of-rnns
• 06_language-model-and-sequence-generation
• 07_sampling-novel-sequences
• 08_vanishing-gradients-with-rnns
• 09_gated-recurrent-unit-gru
• 10_long-short-term-memory-lstm
• 11_bidirectional-rnn
• 12_deep-rnns

$2_{nd}$ week : natural language processing word embeddings

• 01_introduction-to-word-embeddings
  • 01_word-representation
  • 02_using-word-embeddings
  • 03_properties-of-word-embeddings
  • 04_embedding-matrix
• 02_learning-word-embeddings-word2vec-glove
  • 01_learning-word-embeddings
  • 02_word2vec
  • 03_negative-sampling
  • 04_glove-word-vectors
• 03_applications-using-word-embeddings
  • 01_sentiment-classification
  • 02_debiasing-word-embeddings

$3_{rd}$ week : sequence models attention mechanism

• 01_various-sequence-to-sequence-architectures
  • 01_basic-models
  • 02_picking-the-most-likely-sentence
  • 03_beam-search
  • 04_refinements-to-beam-search
  • 05_error-analysis-in-beam-search
  • 06_bleu-score-optional
  • 07_attention-model-intuition
  • 08_attention-model
• 02_speech-recognition-audio-data
  • 01_speech-recognition
  • 02_trigger-word-detection

conclusion of Deep Learning Specialization and thank-you

My personal programming assignments

$1_{st}$ week:

1. Building a Recurrent Neural Network Step by Step
2. Dinosaurus Island Character level language model final
3. Improvise a Jazz Solo with an LSTM Network

$2_{nd}$ week:

1. Word Vector Representation
2. Emojify

$3_{rd}$ week:

1. machine translation
2. Trigger word