Transfer Learning

LLM and Prompt Engineering

AI, ML
Natural Language Processing, NLP, Large Language Models, LLM, Prompt Engineering, Transfer Learning

LLMs and Prompt Engineering #

A Large Language Model extends neural language modelling through much larger datasets, many more parameters, broad pretraining and adaptation to many downstream tasks. Its central operation remains next-token prediction.

Learning Objectives #

  • Explain how neural language modelling develops into an LLM.
  • Describe the meaning of large, general-purpose and pretrained.
  • Explain how a prompt guides generation.
  • Distinguish zero-shot and few-shot prompting.
  • Compare prompting with model adaptation.

Big Picture #

flowchart TD
    A["Broad Text Data"] --> B["Large-scale Pretraining"]
    B --> C["General Language Model"]
    C --> D["Prompt or Adaptation"]
    D --> E["Task Output"]

    style A fill:#E1F5FE
    style B fill:#C8E6C9
    style C fill:#FFF9C4
    style D fill:#EDE7F6
    style E fill:#E1F5FE

1. From Neural Language Models to LLMs ☆ #

A neural language model learns a conditional probability for the next token:

Deep CNN Architectures

April 19, 2026
AI, Deep Learning
Deep Learning, Cnn, Deep CNN Architectures, LeNet, AlexNet, VGG, NiN, GoogLeNet, ResNet, Transfer Learning

Deep CNN Architectures #

Once the basic ideas of convolution, pooling, channels, and classifier heads are understood, the next step is to study how successful CNN architectures are designed in practice. The history of deep CNNs is not just a list of famous models. It is a progression of design ideas: smaller filters, more depth, better optimisation, bottlenecks, multi-scale processing, residual connections, and transfer learning.

Key takeaway:
Deep CNN architectures evolved by solving specific problems one by one: LeNet established the template, AlexNet proved deep learning could dominate large-scale vision, VGG simplified the design, NiN introduced powerful 1 × 1 ideas, GoogLeNet made multi-scale processing efficient, and ResNet solved the optimisation problem of very deep networks.