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Creating Own LLM — Free Course — Fun with Learning Technology

Home›Courses›Creating Own LLM

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Creating Own LLM

A comprehensive curriculum to guide learners from foundational concepts to building and deploying their own large language model (LLM).

36 topics5 sections36 lessons ready

01

Foundations of Machine Learning and NLP

8

Introduction to Machine Learning: Supervised vs. Unsupervised Learning
Basics of Neural Networks and Deep Learning
Natural Language Processing (NLP) Fundamentals
Tokenization and Text Preprocessing Techniques
Word Embeddings: Word2Vec, GloVe, and FastText
Introduction to Transformers and Attention Mechanisms
Overview of Language Models: From N-grams to Neural LMs
Key Papers in NLP: Attention Is All You Need, BERT, and GPT

02

Core Concepts of Large Language Models

8

Architecture of Transformer Models: Encoder-Decoder vs. Decoder-Only
Self-Attention Mechanism and Multi-Head Attention
Positional Encoding and Its Importance
Training Objectives: Language Modeling, Masked Language Modeling, and Next Sentence Prediction
Fine-Tuning vs. Pre-Training: Transfer Learning in NLP
Scaling Laws: Model Size, Data, and Compute
Common LLM Architectures: GPT, BERT, T5, and Their Variants

03

Building and Training Your Own LLM

8

Data Collection and Curation for Training LLMs
Data Cleaning and Preprocessing Pipelines
Choosing the Right Hardware: GPUs, TPUs, and Cloud Resources
Implementing a Transformer Model from Scratch (Using PyTorch or TensorFlow)
Training Loops and Optimization Techniques (Adam, Learning Rate Scheduling)
Distributed Training: Data Parallelism and Model Parallelism
Evaluating Model Performance: Perplexity, BLEU, and Human Evaluation

04

Deployment and Advanced Topics

7

Model Compression: Quantization, Pruning, and Knowledge Distillation
Serving LLMs: APIs, Microservices, and Scalable Inference
Optimizing Inference: ONNX, TensorRT, and Model Optimization Tools
Building a User Interface for Your LLM: Gradio, Streamlit, or Custom Frontend
Monitoring and Maintaining LLMs in Production
Advanced Topics: Reinforcement Learning from Human Feedback (RLHF)
Exploring Open-Source LLM Frameworks: Hugging Face Transformers, DeepSpeed, and Megatron-LM

05

Interview Prep

5

Explain the transformer architecture and its key components.
How do you handle bias and fairness in large language models?
What are the challenges of training large language models at scale?
Describe the process of fine-tuning a pre-trained language model.
How would you deploy a large language model in a production environment?

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Ethical Considerations: Bias, Fairness, and Misuse in LLMs

Fine-Tuning Pre-Trained Models for Specific Tasks