NN and Neural Language Modelling

Neural Networks and Neural Language Modelling #

Neural networks learn useful representations and nonlinear relationships directly from data. In language modelling, they replace discrete N-gram identities with learned word embeddings and use these representations to predict the next word.

Learning Objectives #

• Explain the computation performed by a neural unit.
• Describe why hidden layers and nonlinear activations are needed.
• Explain how feed-forward networks support NLP classification.
• Trace the flow through a feed-forward neural language model.
• Compare N-gram and neural language models.

Big Picture #

flowchart TD
    A["Context Words"] --> B["One-hot Inputs"]
    B --> C["Embedding Lookup"]
    C --> D["Combined Context"]
    D --> E["Hidden Layer"]
    E --> F["Softmax"]
    F --> G["Next-word Probabilities"]

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

1. Neural Network Units ☆ #

A neural unit receives input values, multiplies them by learned weights, adds a bias, and applies an activation function.

Rewards, Returns, Policies and Value Functions

Rewards, Returns, Policies and Value Functions #

An MDP describes how states, actions, rewards and transitions fit together. The next task is to evaluate behaviour: what should the agent try to achieve, how should future rewards be counted, and how good is a state or action over the long term?

Rewards define the objective, returns combine rewards across time, a policy describes behaviour, and value functions predict the long-term quality of that behaviour.

Foundation Models

December 14, 2025

Foundation Model #

AI models trained on massive datasets to perform a wide range of tasks with minimal fine-tuning.

• are large deep learning neural networks

• are large AI models trained on massive and diverse datasets (text, images, audio, or multiple modalities).

• Contain millions or billions of parameters.

• designed to perform a broad range of general tasks

• designed for general-purpose intelligence, not a single task.

• acts as base models for building specialised AI applications

LLM - Model

LLM – Large Language Model #

Large Language Models (LLMs) are advanced AI systems designed to process, understand, and generate human-like text.

They learn language by analysing massive amounts of text data, discovering patterns in:

• grammar

• meaning

• context

• relationships between words and sentences

• Built on Deep Learning

• Implemented using Neural Networks

• Based on Transformers

• Often combined with tools like:

  • Retrieval (RAG)
  • Agents
  • External APIs
  • Memory systems

What makes an LLM special? #

• Built using deep neural networks
• Trained on very large datasets (books, articles, code, web text)
• Can perform many tasks without task-specific training
• General-purpose language understanding, not single-task models

Foundation: Transformer Architecture #

LLMs are based on the Transformer Architecture, which allows models to understand context and long-range dependencies in text.

AI Agents

December 15, 2025

AI Agents #

Also referred to as Agentic AI.

AI agents are intelligent systems that can plan, make decisions, and take actions to achieve goals with minimal human intervention.

• A common use case is task automation

• for example booking travel based on a user’s request.

• AI agents typically build on Generative AI and use Large Language Models (LLMs) as the reasoning core.

• Agents often interact with tools (APIs, databases, calendars) to complete multi-step workflows.

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) #

Retrieval-Augmented Generation (RAG) is a system design pattern that improves an LLM’s answers by:

1. Retrieving relevant information from an external knowledge source, and then
2. Augmenting the LLM prompt with that retrieved context before generating the final response.

RAG helps an LLM look things up first, then answer using evidence.

Why RAG is Useful #

RAG is commonly used when:

• Your knowledge is in private documents (PDFs, policies, internal wiki)
• You need up-to-date information (things not in the model’s training data)
• You want fewer hallucinations by grounding answers in retrieved sources
• You want traceability (show “where the answer came from”)

RAG does not change the model weights.
It changes what the model sees at inference time by adding retrieved context.

Deep Feedforward Neural Networks (DFNN) for Classification

February 26, 2026

Deep Feedforward Neural Networks (DFNN) or Multi Layer Perceptrons (MLP) for Classification #

A Deep Feedforward Neural Network (DFNN), also called a Multi-Layer Perceptron (MLP), is a neural network with one or more hidden layers where information flows forward only (no recurrence).
For classification, DFNNs learn non-linear decision boundaries by combining hidden layers with non-linear activation functions.

Core idea:

• A single neuron can only learn linear boundaries.
• Adding hidden layers + non-linearity allows DFNNs to solve problems like XOR.

MLP as solution for XOR #

A single perceptron fails on XOR because XOR is not linearly separable.

Decision Tree

Decision Tree #

A decision tree classifies an example by asking a sequence of questions about its attributes until it reaches a leaf (final decision).

Key takeaway: A decision tree grows by repeatedly splitting the training data into purer subsets using an impurity measure (Entropy / Gini / Classification Error).

• Information Theory
• Entropy Based Decision Tree Construction
• Avoiding Overfitting
• Minimum Description Length
• Handling Continuous valued attributes, missing attributes

Information Theory #

Decision trees need a way to measure: “How mixed are the class labels at a node?”

LLM and Prompt Engineering

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:

Prediction & Forecasting

Prediction & Forecasting #

Prediction and forecasting use statistical models to estimate unknown or future values.

In this module, the focus is on correlation, regression, and time series forecasting.

Key takeaway:
Prediction estimates a value using a model.

Forecasting is prediction where the order of time matters.

• Correlation
• Regression
• Time series analysis
• Components of time series data
• Moving average and weighted moving average
• AR model
• ARMA model
• ARIMA model
• SARIMA and SARIMAX
• VAR and VARMAX
• Simple exponential smoothing

Prediction vs Forecasting ☆ #

Overall Workflow #

flowchart LR
    A[Data] --> B[Explore Pattern]
    B --> C[Choose Model]
    C --> D[Train or Fit]
    D --> E[Validate]
    E --> F[Predict or Forecast]
    F --> G[Interpret Error]

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

Correlation ☆ #

Correlation measures the direction and strength of linear relationship between two variables.