Getting Started with Big Language Modeling from Scratch

Stage 1: Mathematical Foundations

At the heart of learning the Great Language Model is a mastery of the following mathematical concepts:

1. linear algebra(Matrices, vectors, matrix multiplication, eigenvalues and eigenvectors)
2. differentiation and integration(derivatives, partial derivatives, chain rule)
3. probability and statistics(conditional probability, Bayes' theorem, expected value, variance)
4. make superior(Gradient descent, convex optimization)

Recommended Learning Resources:

• “Mathematics for Machine Learning” Specialized course (offered by Coursera): it is an introductory machine learning-related math foundation course covering linear algebra, calculus, and probability.

• 3Blue1Brown YouTube channel: learn linear algebra and calculus through visual math explanations.

Stage 2: Machine Learning Fundamentals

With a foundation in math, entering the world of machine learning is an important prerequisite for understanding large language models. Provides a very goodmachine learning Specialized courses (Machine Learning Specialization):

1. Machine Learning Specialization
   • academic program1：Supervised Machine Learning: Regression and Classification
     • Explain the basic concepts of machine learning, basic algorithms such as linear regression and logistic regression.
   • academic program2：Advanced Learning Algorithms
     • Learn deep machine learning algorithms such as tree models, clustering algorithms, etc.
   • academic program3：Unsupervised Learning, Recommenders, Reinforcement Learning
     • The concepts of unsupervised learning and reinforcement learning will be helpful in understanding complex models in the future.

Stage 3: Deep Learning

Having mastered the fundamentals of machine learning, moving into deep learning is the key to further learning large language models. TheDeep Learning Specialized Courses (Deep Learning Specialization） Ideal for getting started.

1. Neural Networks and Deep Learning
   • Learn the basic building blocks of neural networks: forward propagation, back propagation, activation functions.
2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
   • Learn how to tune hyperparameters, regularize, and optimize deep neural networks to help you build more efficient models.
3. Structuring Machine Learning Projects
   • Explaining how to design and optimize machine learning projects is important for future project practice.
4. Convolutional Neural Networks
   • An introduction to Convolutional Neural Networks (CNNs), which, although primarily used for image processing, are useful for understanding the deeper concepts of neural networks.
5. Sequence Models
   • Models that focus on processing sequential data, such as RNNs and LSTMs, which are the antecedent foundations of large language models.

Stage 4: Natural Language Processing (NLP)

Natural Language Processing (NLP) is a direct application area for large language modeling. Enter the field of NLP after completing the foundation course in Deep Learning. ProvidesNatural Language Processing Specialization。

1. Classification and Vector Spaces
   • Learn techniques such as text categorization, Word2Vec, and other techniques for transforming text data into vector representations.
2. Sequence Models in NLP
   • Learn how models such as RNN, LSTM, etc. work in natural language processing, especially with sequential data.
3. Attention Models
   • Introduces the Attention mechanism, which is at the heart of large language models (e.g., GPT, BERT).
4. Transformers and Question Answering
   • Learn the Transformer model, which is the basis for most of the current state-of-the-art NLP models, including GPT, BERT, and others.

Stage 5: Large Language Model (LLM)

As a result of the above, you have a foundation for understanding and applying the Large Language Model. Courses specifically focused on large language models are also available:

1. Generative AI with Large Language Models (LLMs)

   • ChatGPT Prompt Engineering for Developers
     • Learn how to write effective prompts to interact with large language models and improve model generation.
   • Building Applications with LLMs
     • Learn how to apply the Big Language Model to real-world projects, such as dialog systems, code generation, and more.

2. Hugging Face Transformer textbook

   • Learn how to fine-tune and deploy your big language models using pre-trained models on the Hugging Face platform.

Phase 6: Project Practices and Large Language Model Security

After learning the theory, hands-on practice is a very important step. You can choose the following project directions to practice:

• Train your own GPT model: Fine-tune existing large language models for specific tasks, e.g., dialog generation, Q&A systems.
• A Study of Security in Large Language Modeling: To study the application of large models in the field of cybersecurity, e.g., against attacks, model poisoning, and privacy protection.