Mastering NLP Fundamentals: A 4-hour Hands-on Tutorial

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Before diving into Large Language Models (LLMs), this video is all you need to watch. We've crafted a complete guide to walk you through the essential prerequisites, starting from foundational NLP concepts, progressing to deep learning fundamentals, and concluding with advanced architectures like Encoder-Decoder models and attention mechanisms.

Whether you're just starting out or looking to reinforce your understanding, this video covers it all-ensuring you're fully prepared to tackle LLMs.

Key Sections Covered:

🔍 Natural Language Processing (NLP)
- Overview of NLP and its components
- Common NLP tasks and challenges
- Techniques like text preprocessing, regex, stemming, lemmatization
- Understanding embeddings: Bag of Words, TF-IDF, Word2Vec

🤖 Machine Learning for NLP
- Naive Bayes sentiment classifier: theory and hands-on coding
- Introduction to deep learning and neural networks
- PyTorch basics, Dataset, and DataLoader explained

🧠 Deep Learning Concepts
- Neural networks: types, architectures, and backpropagation
- Activation functions, loss functions, and optimizers
- Practical coding examples for ANN, RNN, CNN

💡 Advanced Neural Architectures for NLP
- RNNs, LSTMs, GRUs: working, use cases, pros, and cons
- Character-level language modeling with RNNs

📘 Encoder-Decoder Architectures
- Introduction to the Encoder-Decoder model
- Neural Machine Translation with Bahdanau attention
- Training and inference with saved models for translation tasks

🌟 Why Watch This Video?
- Comprehensive: Everything you need to learn LLM prerequisites is here.
- Practical: Code walkthroughs and hands-on examples for every major concept.
- Advanced Coverage: From basic NLP to deep learning to advanced architectures like Encoder-Decoder networks with attention.

By the end of this video, you'll have all the foundational knowledge necessary to excel in the world of LLMs.

Timestamps

0:00:00 LLM Prerequisites Introduction
0:00:30 What is Natural Language Processing
0:01:28 Key Components of NLP
0:05:40 Common NLP Tasks
0:12:30 Techniques / Models in NLP
0:18:11 Challenges in NLP
0:22:17 Applications of NLP
0:25:00 NLP Pipeline - An Overview
0:32:07 Text Processing Methods - Text Normalization, Stemming, Lemmatization, Regex, Stop Words Removal
0:41:40 Regex Text Preprocessing in Detail
0:45:56 Embeddings and Embedding Methods - Bag Of Words, TF-IDF, Word2Vec, Custom Embeddings
0:55:00 Machine Learning For NLP
0:56:47 Naive Bayes Sentiment Classifier Theory
1:00:45 Naive Bayes Sentiment Classifier Code
1:08:57 Intermediate Prerequisites Introduction
1:10:57 Deep Learning Introduction - What, When, Why, How
1:16:27 Pytorch Introduction
1:18:25 Pytorch Functions Overview
1:24:17 Pytorch Dataset and DataLoader
1:27:27 Neural Networks Introduction - What, When, Why, How
1:31:32 Types of NN Architectures - ANN, CNN, RNN
1:34:29 Forward and Backward Propagation Mathematical Intuition
1:50:57 Gradient Descent in Backpropagation
1:55:04 Simple ANN - Theory, Code and Training
2:06:57 Activation Functions in NN - What, Why, How, Types and Code Example
2:19:33 Loss Functions in NN - What, Why, How, Types and Code Example
2:23:54 Optimizers in NN - What, Why, How, Types and Code Example
2:29:33 RNN Networks for NLP Introduction
2:30:57 RNN Introduction, Working, Usecases, Pros and Cons
2:43:57 LSTM Introduction, Working, Usecases, Pros and Cons
2:56:37 BiLSTM Introduction, Working, Usecases, Pros and Cons
3:02:00 GRU Introduction, Working, Usecases, Pros and Cons
3:07:57 RNN Networks for Character Level Story Generation - Language Modeling
3:23:58 Advanced Prerequisites Introduction
3:25:15 Encoder Decoder Network Introduction - What, Why, When
3:31:08 How Encoder Decoder Network Works?
3:39:00 Neural Machine Translation Model Architecture Working Explanation
3:43:16 Bahdanau Attention Working Explanation
3:47:23 Neural Machine Translation Model Architecture Working Explanation - Continued
3:51:58 Training Code Walkthrough
4:02:13 Inferencing Saved Model For Translation - German to English

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