---
# System prepended metadata

title: 'Group 2: Natural Language Processing Fast Ai to practical transformer'

---

# Group 2: Natural Language Processing Fast Ai to practical transformer

Suitable for anyone who have trained deeplearning networks and want to try out NLP 

Suggested Route: 5 hrs a week 
1 hr of lesson
3 hrs of code in notebook 
## Balaji 

Week 1
- [x] 1. What is NLP?
    - [x] A changing field
    - [x] Resources
    - [x] Tools
    - [x] Python libraries
    - [x] Example applications
    - [x] Ethics issues

Week 2
- [x] 2. Topic Modeling with NMF and SVD
    - [x] Stop words, stemming, & lemmatization
    - [x] Term-document matrix
    - [x] Topic Frequency-Inverse Document Frequency (TF-IDF)
    - [x] Singular Value Decomposition (SVD)
    - [x] Non-negative Matrix Factorization (NMF)
    - [x] Truncated SVD, Randomized SVD

Week 3
- [ ] 3. Sentiment classification with Naive Bayes, Logistic regression, and ngrams
    - [x] Sparse matrix storage
    - [x] Counters
    - [x] the fastai library
    - [ ] Naive Bayes
    - [ ] Logistic regression
    - [ ] Ngrams
    - [ ] Logistic regression with Naive Bayes features, with trigrams

Week 4
- [ ] 4. Regex (and re-visiting tokenization)

Week 5
- [ ] Deeplearning.ai Recurrent Neural Networks
- [ ] Deeplearning.ai Natural Language Processing & Word Embeddings

Week 6
- [ ] 5. Language modeling & sentiment classification with deep learning
    - [ ] Language model
    - [ ] Transfer learning
    - [ ] Sentiment classification

Week 7
- [ ] 6. Translation with RNNs
    - [ ] Review Embeddings
    - [ ] Bleu metric
    - [ ] Teacher Forcing
    - [ ] Bidirectional
    - [ ] Attention

Week 8 and 9
- [ ] Deeplearning.ai Sequence models & Attention mechanism
- [ ] 7. Translation with the Transformer architecture
    - [ ] Transformer Model
    - [ ] Multi-head attention
    - [ ] Masking
    - [ ] Label smoothing

Week 10 11
- [ ] Implement a tranformer in tensorflow from scratch 

Week 12
- [ ] 8. Bias & ethics in NLP
    - [ ] bias in word embeddings
    - [ ] types of bias
    - [ ] attention economy
    - [ ] drowning in fraudulent/fake info

## Aditya 

Week 1
- [x] 1. What is NLP?
    - [x] A changing field
    - [x] Resources
    - [x] Tools
    - [x] Python libraries
    - [x] Example applications
    - [x] Ethics issues

Week 2
- [x] 2. Topic Modeling with NMF and SVD
    - [x] Stop words, stemming, & lemmatization
    - [x] Term-document matrix
    - [x] Topic Frequency-Inverse Document Frequency (TF-IDF)
    - [x] Singular Value Decomposition (SVD)
    - [x] Non-negative Matrix Factorization (NMF)
    - [x] Truncated SVD, Randomized SVD

Week 3
- [x] 3. Sentiment classification with Naive Bayes, Logistic regression, and ngrams
    - [x] Sparse matrix storage
    - [x] Counters
    - [x] the fastai library
    - [ ] Naive Bayes
    - [ ] Logistic regression
    - [ ] Ngrams
    - [ ] Logistic regression with Naive Bayes features, with trigrams

Week 4
- [ ] 4. Regex (and re-visiting tokenization)

Week 5
- [ ] Deeplearning.ai Recurrent Neural Networks
- [ ] Deeplearning.ai Natural Language Processing & Word Embeddings

Week 6
- [ ] 5. Language modeling & sentiment classification with deep learning
    - [ ] Language model
    - [ ] Transfer learning
    - [ ] Sentiment classification

Week 7
- [ ] 6. Translation with RNNs
    - [ ] Review Embeddings
    - [ ] Bleu metric
    - [ ] Teacher Forcing
    - [ ] Bidirectional
    - [ ] Attention

Week 8 and 9
- [ ] Deeplearning.ai Sequence models & Attention mechanism
- [ ] 7. Translation with the Transformer architecture
    - [ ] Transformer Model
    - [ ] Multi-head attention
    - [ ] Masking
    - [ ] Label smoothing

Week 10 11
- [ ] Implement a tranformer in tensorflow from scratch 

Week 12
- [ ] 8. Bias & ethics in NLP
    - [ ] bias in word embeddings
    - [ ] types of bias
    - [ ] attention economy
    - [ ] drowning in fraudulent/fake info


## Kaiwei 

Week 1
- [x] 1. What is NLP?
    - [x] A changing field
    - [x] Resources
    - [x] Tools
    - [x] Python libraries
    - [x] Example applications
    - [x] Ethics issues

Week 2
- [x] 2. Topic Modeling with NMF and SVD
    - [x] Stop words, stemming, & lemmatization
    - [x] Term-document matrix
    - [x] Topic Frequency-Inverse Document Frequency (TF-IDF)
    - [x] Singular Value Decomposition (SVD)
    - [x] Non-negative Matrix Factorization (NMF)
    - [x] Truncated SVD, Randomized SVD

Week 3
- [x] 3. Sentiment classification with Naive Bayes, Logistic regression, and ngrams
    - [x] Sparse matrix storage
    - [x] Counters
    - [x] the fastai library
    - [ ] Naive Bayes
    - [ ] Logistic regression
    - [ ] Ngrams
    - [ ] Logistic regression with Naive Bayes features, with trigrams

Week 4
- [ ] 4. Regex (and re-visiting tokenization)

Week 5
- [ ] Deeplearning.ai Recurrent Neural Networks
- [ ] Deeplearning.ai Natural Language Processing & Word Embeddings

Week 6
- [ ] 5. Language modeling & sentiment classification with deep learning
    - [ ] Language model
    - [ ] Transfer learning
    - [ ] Sentiment classification

Week 7
- [ ] 6. Translation with RNNs
    - [ ] Review Embeddings
    - [ ] Bleu metric
    - [ ] Teacher Forcing
    - [ ] Bidirectional
    - [ ] Attention

Week 8 and 9
- [ ] Deeplearning.ai Sequence models & Attention mechanism
- [ ] 7. Translation with the Transformer architecture
    - [ ] Transformer Model
    - [ ] Multi-head attention
    - [ ] Masking
    - [ ] Label smoothing

Week 10 11
- [ ] Implement a tranformer in tensorflow from scratch 

Week 12
- [ ] 8. Bias & ethics in NLP
    - [ ] bias in word embeddings
    - [ ] types of bias
    - [ ] attention economy
    - [ ] drowning in fraudulent/fake info

### Suggestions: 
-dont make it in weeks. Make it in traunches 

## Veri 

Week 1
- [ ] 1. What is NLP?
    - [ ] A changing field
    - [ ] Resources
    - [ ] Tools
    - [ ] Python libraries
    - [ ] Example applications
    - [ ] Ethics issues

Week 2
- [ ] 2. Topic Modeling with NMF and SVD
    - [ ] Stop words, stemming, & lemmatization
    - [ ] Term-document matrix
    - [ ] Topic Frequency-Inverse Document Frequency (TF-IDF)
    - [ ] Singular Value Decomposition (SVD)
    - [ ] Non-negative Matrix Factorization (NMF)
    - [ ] Truncated SVD, Randomized SVD

Week 3
- [ ] 3. Sentiment classification with Naive Bayes, Logistic regression, and ngrams
    - [ ] Sparse matrix storage
    - [ ] Counters
    - [ ] the fastai library
    - [ ] Naive Bayes
    - [ ] Logistic regression
    - [ ] Ngrams
    - [ ] Logistic regression with Naive Bayes features, with trigrams

Week 4
- [ ] 4. Regex (and re-visiting tokenization)

Week 5
- [ ] Deeplearning.ai Recurrent Neural Networks
- [ ] Deeplearning.ai Natural Language Processing & Word Embeddings

Week 6
- [ ] 5. Language modeling & sentiment classification with deep learning
    - [ ] Language model
    - [ ] Transfer learning
    - [ ] Sentiment classification

Week 7
- [ ] 6. Translation with RNNs
    - [ ] Review Embeddings
    - [ ] Bleu metric
    - [ ] Teacher Forcing
    - [ ] Bidirectional
    - [ ] Attention

Week 8 and 9
- [ ] Deeplearning.ai Sequence models & Attention mechanism
- [ ] 7. Translation with the Transformer architecture
    - [ ] Transformer Model
    - [ ] Multi-head attention
    - [ ] Masking
    - [ ] Label smoothing

Week 10 11
- [ ] Implement a tranformer in tensorflow from scratch 

Week 12
- [ ] 8. Bias & ethics in NLP
    - [ ] bias in word embeddings
    - [ ] types of bias
    - [ ] attention economy
    - [ ] drowning in fraudulent/fake info

