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Welcoming you to Journey
into Large Language Models!
Agenda of this Journey
Session1: Intro to NLP
• Data Preprocessing
• Similarities
• Word Embeddings
• Visualization
GRU, RNN, Types of
RNNs, LSTMs , Practical
Transformers, Types of
transformers, Transformer
Architecture
Practical on Finetuning Bert
Transformer using Hugging
Faces Library
Session2: NLP Using Deep Learning Session3: Advanced NLP
Session4: Practical
Introduction to NLP
What , Why , How?
Data Cleaning
• Tokenization
• Stopwords removal
• Stemming
• Lemmatization
• Morphological Segmentation
Vectorization/Embeddings.
Cosine Similarity,
Euclidean distance.
Types of text transformations
• OneHotEncoding (OHE)
• Bag of Words (BOW)
• Word2Vec, AvgWord2vec
Visualization of Word Vectors
• Using t-SNE
What is NLP?
Why NLP?
How NLP works?
Data Preprocessing
Tokenization : conversion of text into tokens.
Ex : GDSC is a university based community
group for students.
LowerCasing:
Ex: SATYA – satya
Stopwords Removal :
Ex: is, a, the, etc.
Stemming: Reducing words to their base or root form
by removing suffixes or prefixes.
Lemmatization(Lemma): Reducing words to their
base or root form by removing suffixes or prefixes.
Difference?
Ex: I am riding my bicycle to the store..
stem:"I am ride my bicycl to the store."
Lemma:"I be ride my bicycle to the store."
Morphological segmentation.
This divides words into smaller parts called
morphemes.
Ex: Untestably - "un," "test," "able" and "ly" as
morphemes (useful in lang translation)
Data Preprocessing
Vectorization/Word Embedding
Frequency/Count Based
• OneHotEncoding
• Bag of Word
• CountVectorizer
• Tf-Idf
• Glove
Predictive Based
• Word2Vec
• CBOW
• Skip-Gram
• AvgWord2Vec
Bag of Words
Link : https://www.analyticsvidhya.com/blog/2020/02/quick-introduction-bag-of-words-bow-tf-idf/
Review 1: This movie is very scary and long
Review 2: This movie is not scary and is slow
Review 3: This movie is spooky and good
Vector of Review 1: [1 1 1 1 1 1 1 0 0 0 0]
Vector of Review 2: [1 1 2 0 0 1 1 0 1 0 0]
Vector of Review 3: [1 1 1 0 0 0 1 0 0 1 1]
Word2Vec
CBOW
Skip-Gram
It’s fun time…huhu!!
3 men are fishing in their boat when a sudden monster wave sends them all
overboard and into the water. Only 1 man got his hair wet. How?
Which word in the dictionary is spelt incorrectly?
Which letter of the alphabet has the most water?
What did the lava say to his girlfriend?
What do you call a guy who’s really loud?
How can machine know two words are similar or not?
Euclidean Distance
Cosine Similarity: joao Filix
Featurized representation of word embedding
It is interesting to know that King - Man + Woman ≈
Queen!
Enough!! Enough saying!
Can you Visualize and do some
practical session??
Questions?
https://forms.gle/TCzWP8mQQ4qpB83r7
Feedback?
Thank You!

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Beyond Words: Journey into Large Language Models(LLMs) - Day-1

  • 1. Welcoming you to Journey into Large Language Models!
  • 2. Agenda of this Journey Session1: Intro to NLP • Data Preprocessing • Similarities • Word Embeddings • Visualization GRU, RNN, Types of RNNs, LSTMs , Practical Transformers, Types of transformers, Transformer Architecture Practical on Finetuning Bert Transformer using Hugging Faces Library Session2: NLP Using Deep Learning Session3: Advanced NLP Session4: Practical
  • 3. Introduction to NLP What , Why , How? Data Cleaning • Tokenization • Stopwords removal • Stemming • Lemmatization • Morphological Segmentation Vectorization/Embeddings. Cosine Similarity, Euclidean distance. Types of text transformations • OneHotEncoding (OHE) • Bag of Words (BOW) • Word2Vec, AvgWord2vec Visualization of Word Vectors • Using t-SNE
  • 4. What is NLP? Why NLP? How NLP works?
  • 5. Data Preprocessing Tokenization : conversion of text into tokens. Ex : GDSC is a university based community group for students. LowerCasing: Ex: SATYA – satya Stopwords Removal : Ex: is, a, the, etc. Stemming: Reducing words to their base or root form by removing suffixes or prefixes.
  • 6. Lemmatization(Lemma): Reducing words to their base or root form by removing suffixes or prefixes. Difference? Ex: I am riding my bicycle to the store.. stem:"I am ride my bicycl to the store." Lemma:"I be ride my bicycle to the store." Morphological segmentation. This divides words into smaller parts called morphemes. Ex: Untestably - "un," "test," "able" and "ly" as morphemes (useful in lang translation) Data Preprocessing
  • 7. Vectorization/Word Embedding Frequency/Count Based • OneHotEncoding • Bag of Word • CountVectorizer • Tf-Idf • Glove Predictive Based • Word2Vec • CBOW • Skip-Gram • AvgWord2Vec
  • 8. Bag of Words Link : https://www.analyticsvidhya.com/blog/2020/02/quick-introduction-bag-of-words-bow-tf-idf/ Review 1: This movie is very scary and long Review 2: This movie is not scary and is slow Review 3: This movie is spooky and good Vector of Review 1: [1 1 1 1 1 1 1 0 0 0 0] Vector of Review 2: [1 1 2 0 0 1 1 0 1 0 0] Vector of Review 3: [1 1 1 0 0 0 1 0 0 1 1]
  • 12. 3 men are fishing in their boat when a sudden monster wave sends them all overboard and into the water. Only 1 man got his hair wet. How?
  • 13. Which word in the dictionary is spelt incorrectly?
  • 14. Which letter of the alphabet has the most water?
  • 15. What did the lava say to his girlfriend?
  • 16. What do you call a guy who’s really loud?
  • 17. How can machine know two words are similar or not?
  • 20. Featurized representation of word embedding It is interesting to know that King - Man + Woman ≈ Queen!
  • 21. Enough!! Enough saying! Can you Visualize and do some practical session??