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NLP with Transformers: Fundamentals and Core Applications

12 chapters and 67 canonical sections synced from the Cuantum content database.

Author
Cuantum Tech.
Chapters
12
Reading time
~ 13h
Level
Professional
Language
English
Edition
2025
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Chapters & sections

12 chapters - 67 sections
Chapter 01

Chapter 1: Introduction to NLP and Its Evolution

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11.1 What is NLP?12m21.2 Historical Development of NLP12m31.3 Traditional Approaches in NLP12m41.4 Practical Exercises for Chapter 112m5Chapter 1 Summary12m
Chapter 02

Chapter 2: Fundamentals of Machine Learning for

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12.1 Basics of Machine Learning for Text12m22.2 Neural Networks in NLP12m32.3 Word Embeddings: Word2Vec, GloVe, and FastText12m42.4 Introduction to Transformer-based Embeddings12m6Chapter Summary12m5Practical Exercises for Chapter 212m
Chapter 03

Chapter 3: Attention and the Rise of Transformers

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13.1 Challenges with RNNs and CNNs in NLP12m23.2 Understanding Attention Mechanisms12m33.3 Self-Attention and Multi-Head Attention12m43.4 Sparse Attention for Efficiency12m6Chapter 3 Summary12m5Practical Exercises for Chapter 312m
Chapter 04

Quiz Part I

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2Answers12m1Questions12m
Chapter 05

Chapter 4: The Transformer Architecture

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14.1 The "Attention Is All You Need" Paper12m24.2 Encoder-Decoder Framework Explained12m34.3 Positional Encoding and Its Importance12m44.4 Comparisons with Traditional Architectures12m6Chapter Summary12m5Practical Exercises for Chapter 412m
Chapter 06

Chapter 5: Key Transformer Models and Innovations

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15.1 BERT and Variants (RoBERTa, DistilBERT)12m25.2 GPT and Autoregressive Transformers12m35.3 Multimodal Transformers: CLIP, DALL-E12m45.4 Specialized Models: BioBERT, LegalBERT12m6Chapter Summary12m5Practical Exercises for Chapter 512m
Chapter 07

Quiz Part II

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2Answers12m1Questions12m
Chapter 08

Chapter 6: Core NLP Applications

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16.1 Sentiment Analysis12m26.2 Named Entity Recognition (NER)12m36.3 Text Classification12m5Chapter 6 Summary12m4Practical Exercises for Chapter 612m
Chapter 09

Project 1: Sentiment Analysis with BERT

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11. Why Sentiment Analysis?12m22. Why Use BERT?12m33. Project Overview12m44. Step 1: Preparing the Environment12m55. Step 2: Loading and Exploring the Dataset12m66. Step 3: Tokenizing the Dataset12m77. Step 4: Fine-Tuning BERT12m88. Step 5: Evaluating the Model12m99. Step 6: Using the Model for Prediction12m1010. Conclusion12m
Chapter 10

Project 2: News Categorization Using BERT

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11. Why BERT for News Categorization?12m22. What Will You Learn?12m33. Step 1: Setting Up the Environment12m44. Step 2: Loading and Preparing the Dataset12m55. Preprocess the Dataset12m66. Step 3: Fine-Tuning BERT for News Categorization12m77. Step 4: Evaluating the Model12m88. Step 5: Testing with New Data12m9Conclusion12m
Chapter 11

Project 3: Customer Feedback Analysis Using Sentiment Analysis

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11. Why Sentiment Analysis?12m22. What Will You Learn?12m33. Step 1: Setting Up the Environment12m44. Step 2: Loading and Preparing the Dataset12m55. Step 3: Fine-Tuning BERT for Sentiment Analysis12m66. Step 4: Evaluating the Model12m77. Step 5: Testing with Real Customer Feedback12m8Conclusion12m
Chapter 12

Quiz Part III

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2Answers Key12m1Questions12m