🧩 Part 1/12

✔ What is PyTorch?
✔ Why use PyTorch for NLP?
✔ Dynamic computation graphs
✔ Rich set of libraries and tools
✔ Easy integration with other frameworks
✔ How to install PyTorch?
✔ Check the system requirements
✔ Choose the right package manager
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🧩 Part 2/12

✔ What are Tensors?
✔ How to Create Tensors in PyTorch
✔ How to Manipulate Tensors in PyTorch
✔ Slicing and Indexing Tensors
✔ Reshaping Tensors
✔ Broadcasting Tensors
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🧩 Part 3/12

✔ Loading and Exploring Text Data
✔ Preprocessing Text Data
✔ Tokenizing Text Data
✔ Encoding Text Data
✔ Using torchtext for Text Data
✔ Building a Text Classifier with PyTorch
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🧩 Part 4/12

✔ PyTorch Basics
✔ Tensors
✔ Autograd
✔ Modules and Functions
✔ Dataset Preparation
✔ Loading the Dataset
✔ Tokenization and Encoding
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🧩 Part 5/12

✔ Data Preparation
✔ Loading and Exploring the Dataset
✔ Preprocessing and Tokenizing the Text
✔ Creating Vocabulary and Encoding the Text
✔ Model Building
✔ Defining the RNN Architecture
✔ Initializing the Model Parameters
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🧩 Part 6/12

✔ What are word embeddings and why are they useful?
✔ How to create word embeddings in PyTorch
✔ What is Word2Vec and how does it work?
✔ How to train a Word2Vec model in PyTorch
✔ How to use the Word2Vec model to find similar words and analogies
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🧩 Part 7/12

✔ Sequence to Sequence Models
✔ Encoder-Decoder Architecture
✔ Teacher Forcing and Scheduled Sampling
✔ Attention Mechanisms
✔ Dot-Product Attention
✔ Multi-Head Attention
✔ Machine Translation with PyTorch
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🧩 Part 8/12

✔ What are transformers and why are they important for NLP?
✔ The transformer architecture
✔ The attention mechanism
✔ What is BERT and how does it work?
✔ The BERT pre-training process
✔ The BERT fine-tuning process
✔ How to use PyTorch and HuggingFace to implement transformers and BERT
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🧩 Part 9/12

✔ What is GPT-2 and why is it important for NLP?
✔ How to install and import PyTorch and HuggingFace transformers
✔ How to load and use a pre-trained GPT-2 model
✔ How to fine-tune GPT-2 on your own text data
✔ How to generate text with GPT-2 using different decoding strategies
✔ How to evaluate the quality and diversity of the generated text
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🧩 Part 10/12

✔ What is Question Answering?
✔ What is BERT and How Does It Work?
✔ How to Use HuggingFace Transformers Library for Question Answering
✔ How to Fine-Tune BERT on SQuAD Dataset
✔ How to Evaluate BERT on Question Answering Tasks
✔ How to Deploy BERT for Question Answering on Web Applications
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🧩 Part 11/12

✔ What is Text Summarization?
✔ Extractive Summarization
✔ Abstractive Summarization
✔ What is BART?
✔ How to Load BART with HuggingFace
✔ How to Fine-Tune BART for Text Summarization
✔ How to Evaluate BART for Text Summarization
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🧩 Part 12/12

✔ Building a NLP Model with PyTorch
✔ Loading and Preprocessing the Data
✔ Defining the Model Architecture
✔ Training and Evaluating the Model
✔ Creating a Web App with Flask
✔ Setting Up the Flask Environment
✔ Designing the Web Interface
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🧩 Part 11/12

✔ What is Text Summarization?
✔ Extractive Summarization
✔ Abstractive Summarization
✔ What is BART?
✔ How to Load BART with HuggingFace
✔ How to Fine-Tune BART for Text Summarization
✔ How to Evaluate BART for Text Summarization
...

🧩 Part 12/12

✔ Building a NLP Model with PyTorch
✔ Loading and Preprocessing the Data
✔ Defining the Model Architecture
✔ Training and Evaluating the Model
✔ Creating a Web App with Flask
✔ Setting Up the Flask Environment
✔ Designing the Web Interface
...