- Introduction
- Course Overview
- Environment Setup
- Alternative Local Setup
- Alternative Colab Setup
- CUDA Setup
Online
₹ 449 2,599
Quick facts
particular | details | |
---|---|---|
Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
₹ 449 ₹2,599
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Introduction
NLP and Transformers
- The Three Eras of AI
- Pros and Cons of Neural AI
- Word Vectors
- Recurrent Neural Networks
- Long Short-Term Memory
- Encoder-Decoder Attention
- Self-Attention
- Multi-head Attention
- Positional Encoding
- Transformer Heads
Preprocessing for NLP
- Stopwords
- Tokens Introduction
- Model-Specific Special Tokens
- Stemming
- Lemmatization
- Unicode Normalization - Canonical and Compatibility Equivalence
- Unicode Normalization - Composition and Decomposition
- Unicode Normalization - NFD and NFC
- Unicode Normalization - NFKD and NFKC
Attention
- Attention Introduction
- Alignment With Dot-Product
- Dot-Product Attention
- Self Attention
- Bidirectional Attention
- Multi-head and Scaled Dot-Product Attention
Language Classification
- Introduction to Sentiment Analysis
- Prebuilt Flair Models
- Introduction to Sentiment Models With Transformers
- Tokenization And Special Tokens For BERT
- Making Predictions
[Project] Sentiment Model With TensorFlow and Transformers
- Project Overview
- Getting the Data (Kaggle API)
- Preprocessing
- Building a Dataset
- Dataset Shuffle, Batch, Split, and Save
- Build and Save
- Loading and Prediction
Long Text Classification With BERT
- Classification of Long Text Using Windows
- Window Method in PyTorch
Named Entity Recognition (NER)
- Introduction to spaCy
- Extracting Entities
- NER Walkthrough
- Authenticating With The Reddit API
- Pulling Data With The Reddit API
- Extracting ORGs From Reddit Data
- Getting Entity Frequency
- Entity Blacklist
- NER With Sentiment
- NER With roBERTa
Question and Answering
- Open Domain and Reading Comprehension
- Retrievers, Readers, and Generators
- Intro to SQuAD 2.0
- Processing SQuAD Training Data
- (Optional) Processing SQuAD Training Data with Match-Case
- Processing SQuAD Dev Data
- Our First Q&A Model
Metrics For Language
- Q&A Performance With Exact Match (EM)
- Introducing the ROUGE Metric
- ROUGE in Python
- Applying ROUGE to Q&A
- Recall, Precision and F1
- Longest Common Subsequence (LCS)
Reader-Retriever QA With Haystack
- Intro to Retriever-Reader and Haystack
- What is Elasticsearch?
- Elasticsearch Setup (Windows)
- Elasticsearch Setup (Linux)
- Elasticsearch in Haystack
- Sparse Retrievers
- Cleaning the Index
- Implementing a BM25 Retriever
- What is FAISS?
- Further Materials for Faiss
- FAISS in Haystack
- What is DPR?
- The DPR Architecture
- Retriever-Reader Stack
[Project] Open-Domain QA
- ODQA Stack Structure
- Creating the Database
- Building the Haystack Pipeline
Similarity
- Introduction to Similarity
- Extracting The Last Hidden State Tensor
- Sentence Vectors With Mean Pooling
- Using Cosine Similarity
- Similarity With Sentence-Transformers
- Further Learning
Pre-Training Transformer Models
- Visual Guide to BERT Pretraining
- Introduction to BERT For Pretraining Code
- BERT Pretraining - Masked-Language Modeling (MLM)
- BERT Pretraining - Next Sentence Prediction (NSP)
- The Logic of MLM
- Pre-training with MLM - Data Preparation
- Pre-training with MLM - Training
- Pre-training with MLM - Training with Trainer
- The Logic of NSP
- Pre-training with NSP - Data Preparation
- Pre-training with NSP - DataLoader
- Setup the NSP Pre-training Training Loop
- The Logic of MLM and NSP
- Pre-training with MLM and NSP - Data Preparation
- Setup DataLoader and Model Pre-training For MLM and NSP