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Complete LLMs Model Course Outline - Bangla Tutorial

Ibrahim Khan November 22, 2025

Complete LLM Model Course Outline

Module 1: Foundations & Prerequisites

1.1 Machine Learning Basics

  • Supervised vs unsupervised learning
  • Training, validation, and test sets
  • Loss functions and optimization
  • Gradient descent and backpropagation
  • Overfitting and regularization

1.2 Neural Networks Fundamentals

  • Perceptrons and multi-layer networks
  • Activation functions (ReLU, sigmoid, tanh)
  • Forward and backward propagation
  • Weight initialization
  • Batch normalization

1.3 Deep Learning Concepts

  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs, LSTM, GRU)
  • Sequence-to-sequence models
  • Encoder-decoder architecture

Module 2: Natural Language Processing Basics

2.1 NLP Fundamentals

  • Tokenization and text preprocessing
  • Lemmatization and stemming
  • Part-of-speech tagging
  • Named entity recognition
  • Sentiment analysis

2.2 Word Representations

  • One-hot encoding
  • Bag-of-words model
  • TF-IDF
  • Word embeddings (Word2Vec, GloVe, FastText)
  • Contextual embeddings

2.3 Language Modeling

  • N-gram language models
  • Probability and perplexity
  • Chain rule of probability
  • Language model evaluation metrics

Module 3: Transformer Architecture

3.1 Attention Mechanism

  • Query, Key, Value framework
  • Attention computation and softmax
  • Multi-head attention
  • Attention visualization and interpretation
  • Why attention matters for NLP

3.2 Transformer Building Blocks

  • Positional encoding and embeddings
  • Feed-forward networks
  • Layer normalization
  • Residual connections
  • Self-attention vs cross-attention

3.3 Encoder-Decoder Transformers

  • BERT-style encoders
  • GPT-style decoders
  • Autoregressive vs autoencoding models
  • Masked language modeling
  • Causal masking for generation

3.4 Hands-On: Building a Simple Transformer

  • Implementing attention from scratch
  • Building a mini transformer model
  • Training on a small dataset
  • Visualization of learned patterns

Module 4: Large Language Model Architecture

4.1 LLM Design Principles

  • Scaling laws and model size
  • Vocabulary and tokenization strategies
  • Embedding dimensions and hidden states
  • Number of layers and heads

4.2 GPT-Style Models (Autoregressive)

  • Decoder-only architecture
  • Causal self-attention
  • Token prediction and generation
  • Sliding window attention and rotary embeddings

4.3 BERT-Style Models (Autoencoding)

  • Encoder-only architecture
  • Bidirectional context
  • Masked language modeling objectives
  • Use cases and limitations

4.4 Hybrid and Novel Architectures

  • T5 (encoder-decoder)
  • Mixtured Experts (MoE)
  • Retrieval-augmented architectures
  • State-space models (Mamba, SSM)

Module 5: Training Large Language Models

5.1 Pretraining Fundamentals

  • Data collection and quality
  • Data preprocessing at scale
  • Tokenization algorithms (BPE, WordPiece)
  • Vocabulary size selection

5.2 Training Objectives

  • Language modeling loss
  • Next sentence prediction
  • Causal language modeling
  • Masked language modeling
  • Contrastive learning objectives

5.3 Scaling and Optimization

  • Distributed training (data parallelism, model parallelism, pipeline parallelism)
  • Mixed precision training
  • Gradient accumulation
  • Learning rate schedules
  • Warmup and annealing

5.4 Scaling Laws

  • Compute-optimal training
  • Performance vs model size
  • Data scaling vs parameter scaling
  • Chinchilla and Compute-Optimal scaling

5.5 Infrastructure and Tools

  • Hardware requirements (GPUs, TPUs)
  • Deep learning frameworks (PyTorch, JAX, TensorFlow)
  • Distributed training libraries
  • Monitoring and logging

Module 6: Fine-tuning and Adaptation

6.1 Full Fine-tuning

  • Task-specific adaptation
  • Transfer learning for NLP
  • Hyperparameter tuning
  • Avoiding catastrophic forgetting
  • Evaluation on downstream tasks

6.2 Parameter-Efficient Fine-tuning

  • LoRA (Low-Rank Adaptation)
  • QLoRA (Quantized LoRA)
  • Prefix tuning
  • Adapter modules
  • BitFit
  • Comparison and trade-offs

6.3 Instruction Tuning

  • Instruction-following datasets
  • Multi-task instruction tuning
  • Chain-of-thought prompting data
  • Alignment objectives
  • RLHF (Reinforcement Learning from Human Feedback)

6.4 Domain Adaptation

  • Continual learning
  • Domain-specific pretraining
  • Few-shot and zero-shot adaptation
  • Meta-learning approaches

Module 7: Prompting and In-Context Learning

7.1 Prompt Engineering Fundamentals

  • Zero-shot prompting
  • Few-shot prompting
  • Prompt structure and design
  • Token efficiency

7.2 Advanced Prompting Techniques

  • Chain-of-thought (CoT) prompting
  • Tree-of-thought reasoning
  • Self-consistency
  • Role-based prompting
  • Multi-step reasoning

7.3 In-Context Learning

  • How LLMs learn from examples
  • Statistical patterns in prompts
  • Example selection strategies
  • Prompt ordering effects
  • Limitations and failure cases

7.4 Prompt Optimization

  • Automated prompt engineering
  • Gradient-based prompt optimization
  • Evolutionary prompt design
  • Human-in-the-loop prompt refinement

Module 8: Retrieval-Augmented Generation (RAG)

8.1 RAG Fundamentals

  • Why external retrieval matters
  • Retrieval vs generation trade-offs
  • Document chunking strategies
  • Embedding-based retrieval

8.2 RAG Architectures

  • Naive RAG
  • Advanced RAG
  • Adaptive RAG with routing
  • Multi-hop retrieval
  • Iterative retrieval refinement

8.3 Vector Databases and Search

  • Vector embeddings
  • Similarity measures (cosine, Euclidean, dot product)
  • Approximate nearest neighbor search
  • Vector database tools (Pinecone, Weaviate, Milvus)
  • Hybrid search (dense + sparse retrieval)

8.4 Evaluation and Optimization

  • Retrieval metrics (precision, recall, NDCG)
  • End-to-end RAG evaluation
  • Query expansion
  • Reranking strategies
  • Knowledge base quality

Module 9: Evaluation and Benchmarking

9.1 Evaluation Metrics

  • Automatic metrics (BLEU, ROUGE, METEOR, BERTScore)
  • Task-specific metrics (accuracy, F1, exact match)
  • Semantic similarity measures
  • Limitations of automatic metrics

9.2 Human Evaluation

  • Annotation guidelines
  • Inter-annotator agreement
  • Crowdsourcing best practices
  • Bias in human evaluation

9.3 Popular Benchmarks

  • GLUE and SuperGLUE (general language understanding)
  • SQuAD and reading comprehension
  • MMLU (knowledge across domains)
  • BigBench (diverse tasks)
  • Custom benchmark design

9.4 Red Teaming and Robustness

  • Adversarial examples
  • Out-of-distribution testing
  • Bias and fairness evaluation
  • Toxicity detection
  • Jailbreak resistance

Module 10: Deployment and Optimization

10.1 Model Compression

  • Quantization (INT8, INT4, INT2)
  • Pruning and sparsity
  • Knowledge distillation
  • Low-rank decomposition

10.2 Inference Optimization

  • Batch inference
  • Token streaming and chunking
  • KV-cache optimization
  • Flash Attention for speed
  • Speculative decoding

10.3 Serving and Infrastructure

  • Model serving frameworks (vLLM, TensorRT-LLM, Ollama)
  • API design patterns
  • Load balancing
  • Cost optimization
  • Latency vs throughput trade-offs

10.4 Monitoring and Maintenance

  • Performance monitoring
  • Drift detection
  • A/B testing
  • Model versioning
  • Continuous deployment

Module 11: Safety, Alignment, and Ethics

11.1 LLM Safety Concerns

  • Hallucinations and factuality
  • Bias and fairness
  • Toxicity and harmful content
  • Jailbreaking and adversarial attacks
  • Privacy and data leakage

11.2 Alignment Techniques

  • RLHF (Reinforcement Learning from Human Feedback)
  • DPO (Direct Preference Optimization)
  • IPO and other alignment methods
  • Constitutional AI
  • Instruction following

11.3 Bias and Fairness

  • Sources of bias in LLMs
  • Stereotypes in language models
  • Evaluation for bias
  • Mitigation strategies
  • Inclusive data collection

11.4 Ethical Considerations

  • Responsible AI principles
  • Environmental impact and carbon footprint
  • Model governance
  • Transparency and interpretability
  • Societal implications

Module 12: Real-World Applications

12.1 Text Generation Applications

  • Content creation and summarization
  • Machine translation
  • Code generation
  • Creative writing and storytelling
  • Question answering

12.2 Classification and Understanding Tasks

  • Sentiment analysis
  • Intent classification
  • Document categorization
  • Topic modeling
  • Information extraction

12.3 Conversational AI

  • Chatbot design
  • Dialogue systems
  • Multi-turn conversation
  • Context management
  • User experience considerations

12.4 Specialized Domains

  • Medical and healthcare applications
  • Legal document analysis
  • Scientific research assistance
  • Financial analysis
  • Customer support automation

Module 13: Emerging Trends and Future Directions

13.1 Multimodal LLMs

  • Vision-language models (CLIP, DALL-E, GPT-4V)
  • Audio-language integration
  • Cross-modal alignment
  • Unified representations

13.2 Long Context and Extended Attention

  • Context window extensions
  • Hierarchical processing
  • Retrieval augmentation for long documents
  • Memory augmentation
  • Recurrent processing

13.3 Reasoning and Agentic Systems

  • Planning and reasoning
  • Tool use and API calling
  • Multi-agent systems
  • Autonomous agents
  • Embodied AI

13.4 Efficiency and Scaling

  • Sparse models and MoE evolution
  • Knowledge distillation advances
  • Hardware-software co-design
  • Federated learning
  • On-device models

Module 14: Hands-On Capstone Project

14.1 Project Options

  • Build and fine-tune a custom LLM
  • Create a RAG-based question answering system
  • Develop a domain-specific chatbot
  • Implement a content generation pipeline
  • Build an AI agent for a specific task

14.2 Project Components

  • Problem definition and data collection
  • Model selection and setup
  • Training and evaluation
  • Optimization and deployment
  • Documentation and presentation

14.3 Best Practices

  • Version control and reproducibility
  • Experiment tracking
  • Model cards and documentation
  • Testing and validation
  • Ethical considerations

Learning Resources by Module

  • Code implementations and notebooks
  • Research paper readings
  • Tutorial videos
  • Dataset repositories
  • Tool and library documentation
  • Community forums and discussions

Assessment Methods

  • Quizzes after each module
  • Hands-on coding assignments
  • Project milestones
  • Final capstone project
  • Peer review components

Estimated Duration

  • Beginner Path: 12-16 weeks (20-25 hours/week)
  • Intermediate Path: 8-12 weeks (25-30 hours/week)
  • Advanced Path: 6-8 weeks (30+ hours/week)

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