Topics:

• 1.1 Linear Algebra for LLMs

  Matrix operations

  Eigenvalues

  Tensor mathematics

• 1.2 Calculus and Optimization

  Gradients

  Backpropagation

  Convergence theory

• 1.3 Probability and Information Theory

  Distributions

  Entropy

  KL divergence

• 1.4 Advanced Python Programming

  OOP

  Async patterns

  Memory management

• 1.5 Development Environment Setup

  GPU/TPU

  Docker

  Experiment tracking

Topics:

• 2.1 Neural Network Architectures and Backpropagation

• 2.2 Natural Language Processing Basics

  Tokenization

  Embeddings

  Language modeling

• 2.3 PyTorch for LLMs

  Tensor operations

  Autograd

  Custom layers

• 2.4 Sequence Modeling Evolution

  RNNs

  LSTMs

  Attention revolution

• 2.5 Evaluation Metrics and Benchmarking

Topics:

• 3.1 Self-Attention Mechanism

  Query-Key-Value

  Scaled dot-product

• 3.2 Multi-Head Attention

  Parallel computation

  Head concatenation

• 3.3 Positional Encodings

  Sinusoidal

  Learned

  RoPE

  ALiBi

• 3.4 Complete Transformer Components

  Layer norm

  FFN

  Residual connections

• 3.5 Architecture Variants

  Encoder-only

  Decoder-only

  Encoder-Decoder

Topics:

• 4.1 GPT Family Evolution

  GPT-3

  GPT-3.5

  GPT-4

  Architectural innovations

• 4.2 Claude, Gemini, and PaLM Architectures

• 4.3 Open Source Models

  LLaMA

  Mistral

  Falcon

  Qwen

• 4.4 Mixture of Experts (MoE) Systems

• 4.5 Efficient Attention Mechanisms

  Flash Attention

  Linear Attention

Topics:

• 5.1 Long Context Innovations

  YaRN

  Positional interpolation

• 5.2 State Space Models

  Mamba

  RWKV

• 5.3 Model Scaling Laws

  Chinchilla optimal

  Compound scaling

• 5.4 Memory-Efficient Architectures

• 5.5 Hardware-Aware Design Patterns

Topics:

• 6.1 Data Collection Strategies

  Web crawling

  Multilingual data

  Domain corpora

• 6.2 Data Processing Pipeline

  Deduplication

  Quality filtering

  PII removal

• 6.3 Tokenization Methods

  BPE

  SentencePiece

  Custom tokenizers

• 6.4 Dataset Mixing and Curriculum Learning

• 6.5 Data Quality Metrics and Analysis

Topics:

• 7.1 Language Modeling Objectives

  CLM

  MLM

  T5 span corruption

  FIM

• 7.2 Loss Functions and Perplexity

• 7.3 Optimization Algorithms

  AdamW

  LAMB

  8-bit optimizers

• 7.4 Learning Rate Schedules and Warm-up Strategies

• 7.5 Training Dynamics and Stability

Topics:

• 8.1 Parallelism Strategies

  Data

  Model

  Pipeline

  Tensor

  3D parallelism

• 8.2 Distributed Frameworks

  PyTorch FSDP

  DeepSpeed ZeRO

  Megatron-LM

• 8.3 Memory Optimization

  Gradient checkpointing

  Mixed precision

  CPU offloading

• 8.4 Multi-node Training and Communication

  NCCL

  InfiniBand

• 8.5 Monitoring and Debugging Distributed Training

Topics:

• 9.1 Hardware Considerations

  GPU/TPU selection

  Memory requirements

• 9.2 Cluster Management

  SLURM

  Kubernetes

  Resource allocation

• 9.3 Data Infrastructure

  High-performance storage

  Streaming datasets

• 9.4 Cost Optimization

  Spot instances

  Resource utilization

• 9.5 Reproducibility and Experiment Management

Topics:

• 10.1 Code Organization and Modular Architecture

• 10.2 Configuration Management and Version Control

• 10.3 Continuous Training Pipelines

• 10.4 Team Collaboration and Knowledge Transfer

• 10.5 Documentation and Testing Strategies

Topics:

• 11.1 Task-Specific Fine-tuning Methods

• 11.2 Multi-Task and Sequential Learning

• 11.3 Domain Adaptation Techniques

• 11.4 Catastrophic Forgetting Mitigation

• 11.5 Training Hyperparameter Optimization

Topics:

• 12.1 LoRA and QLoRA Implementation

• 12.2 Prefix and Prompt Tuning

• 12.3 Adapter Layers and IA³

• 12.4 PEFT Method Comparison and Selection

• 12.5 Memory and Compute Efficiency Analysis

Topics:

• 13.1 Instruction Dataset Creation and Curation

• 13.2 Supervised Fine-tuning (SFT) for Instructions

• 13.3 Multi-turn Dialogue Training

• 13.4 Chain-of-Thought Training

• 13.5 Popular Instruction Datasets (Alpaca, ShareGPT, FLAN)

Topics:

• 14.1 Reward Model Training

• 14.2 PPO Implementation for LLMs

• 14.3 Alternative Methods (DPO, Constitutional AI, RLAIF)

• 14.4 Preference Data Collection

• 14.5 Policy Optimization and Stability

Topics:

• 15.1 Vertical Fine-tuning (Medical, Legal, Financial, Scientific)

• 15.2 Multilingual and Cross-lingual Adaptation

• 15.3 Continual and Few-shot Learning

• 15.4 Multimodal Extensions

• 15.5 Code Generation Specialization

Topics:

• 16.1 Quantization Fundamentals

  INT8, INT4, symmetric/asymmetric

• 16.2 Advanced Methods

  GPTQ

  AWQ

  SmoothQuant

• 16.3 Mixed Precision Training and Inference

• 16.4 Implementation Tools

  PyTorch

  ONNX

  TensorRT

• 16.5 Quality-Performance Trade-offs

Topics:

• 17.1 Pruning Methods

  Magnitude

  Structured

  Gradual

• 17.2 Knowledge Distillation

• 17.3 Neural Architecture Search for LLMs

• 17.4 Compilation and Graph Optimization

• 17.5 Kernel Fusion and Optimization

Topics:

• 18.1 Attention Optimization

  Flash Attention

  PagedAttention

• 18.2 KV Cache Management and Compression

• 18.3 Speculative Decoding

• 18.4 Batch Processing Strategies

• 18.5 Memory-Speed Trade-offs

Topics:

• 19.1 Serving Frameworks

  vLLM

  TGI

  TensorRT-LLM

• 19.2 API Design

  REST

  WebSocket

  gRPC

  Streaming

• 19.3 Scaling Strategies

  Horizontal scaling

  Load balancing

• 19.4 Performance Monitoring and Metrics

• 19.5 Cost Optimization at Scale

Topics:

• 20.1 Model Optimization for Edge Devices

• 20.2 Mobile and Embedded Deployment

• 20.3 Latency Optimization Techniques

• 20.4 Resource-Constrained Inference

• 20.5 Offline Capabilities

Topics:

• 21.1 Retrieval Strategies and Vector Databases

  Vector Indexing

  Similarity Search

  Dense vs Sparse Retrieval

• 21.2 Context Management and Chunking

  Text Splitting Techniques

  Windowing Strategies

  Context Length Handling

• 21.3 Answer Generation and Citation

  Citation Handling

  Hallucination Reduction

  Context-Aware Responses

• 21.4 Hybrid Search Methods

  Combining Dense and Sparse Search

  Weighted Retrieval Approaches

  Cross-Encoder Ranking

• 21.5 RAG System Optimization

  Latency Optimization

  Index Refreshing

  Scaling RAG Pipelines

Topics:

• 22.1 Tool Integration and Function Calling

  API Integration

  Dynamic Tool Use

  Function Calling Frameworks

• 22.2 Memory Systems and State Management

  Short-Term Memory

  Long-Term Memory

  State Persistence

• 22.3 Planning and Reasoning Modules

  Chain-of-Thought Planning

  Hierarchical Task Planning

  Reasoning Engines

• 22.4 Multi-Agent Coordination

  Collaborative Agents

  Task Delegation

  Agent Communication Protocols

• 22.5 Action Execution Frameworks

  Execution Engines

  Error Handling

  Monitoring Actions

Topics:

• 23.1 Dialogue Management

  Rule-Based Dialogue

  LLM-Driven Dialogue

  Hybrid Dialogue Systems

• 23.2 Context Tracking and Session Management

  Session Persistence

  Conversation History

  Multi-Turn Tracking

• 23.3 Persona Consistency

  Persona Design

  Consistency Mechanisms

  Style Transfer

• 23.4 Error Recovery and Fallbacks

  Fallback Strategies

  Clarification Prompts

  Error Correction

• 23.5 Real-time Interaction Optimization

  Streaming Responses

  Low Latency Techniques

  Scalable Conversations

Topics:

• 24.1 Authentication and Authorization

  OAuth2 and JWT

  Role-Based Access Control

  Single Sign-On

• 24.2 Audit Logging and Compliance

  Activity Logging

  Compliance Frameworks

  Regulatory Standards

• 24.3 Data Privacy and Security

  PII Handling

  Encryption Techniques

  GDPR/CCPA Compliance

• 24.4 API Gateway Integration

  Rate Limiting

  Request Routing

  Authentication at Gateway

• 24.5 Monitoring and Alerting Systems

  System Metrics

  Error Tracking

  Real-time Alerts

Topics:

• 25.1 Code Generation Systems

  Code Synthesis

  Bug Fixing

  Code Review with AI

• 25.2 Multimodal Applications

  Text-to-Image

  Speech-to-Text

  Vision-Language Models

• 25.3 Streaming and Real-time Processing

  Streaming Data Pipelines

  Low Latency APIs

  Real-time Model Serving

• 25.4 Hybrid ML/LLM Systems

  Combining Classical ML with LLMs

  Rule-Based + AI Systems

  Pipeline Orchestration

• 25.5 Custom Domain Applications

  Domain-Specific Tuning

  Specialized Datasets

  Industry-Specific Solutions

Topics:

• 26.1 Automatic Evaluation Metrics

  Perplexity

  BLEU

  BERTScore

• 26.2 Benchmark Suites

  MMLU

  HellaSwag

  HumanEval

• 26.3 Human Evaluation Protocols

• 26.4 A/B Testing Frameworks

• 26.5 Task-Specific Evaluation Design

Topics:

• 27.1 Toxicity and Bias Detection

• 27.2 Hallucination Mitigation

• 27.3 Factuality Checking Systems

• 27.4 Adversarial Testing

• 27.5 Red Teaming Methodologies

Topics:

• 28.1 Testing Strategies for LLMs

• 28.2 Integration and Regression Testing

• 28.3 Performance Testing at Scale

• 28.4 Security Testing

• 28.5 Continuous Evaluation Pipelines

Topics:

• 29.1 Emerging Architectures

  Next-gen transformers

• 29.2 Training Innovations

  Flash Attention 3

  Ring Attention

• 29.3 Extreme Optimization

  1-bit LLMs

  Sparse models

• 29.4 AGI Progress and Reasoning

• 29.5 Multimodal and Embodied Intelligence

Topics:

• 30.1 Project 1

  Train Your Own LLM (Complete pipeline from data to deployment)

• 30.2 Project 2

  Enterprise RAG Platform (Production-grade system)

• 30.3 Project 3

  Specialized Domain LLM (Healthcare/Finance/Legal)

• 30.4 Project 4

  High-Performance Inference System

• 30.5 Project 5

  Research Paper Implementation