February 26, 2026Gradient Descent Algorithm (GDA) is
In deep learning, the default training approach is almost always mini-batch gradient descent, usually with Adam or SGD + momentum.
Gradient Descent is used in both regression and classification.
It’s not tied to the task type — it’s tied to the fact you have:
February 21, 2026Gradient descent is an iterative optimisation method used to minimise the regression cost function by repeatedly updating parameters in the direction that reduces error.
Key takeaway: Gradient descent starts with initial parameter values and repeatedly updates them using the gradient until the cost stops decreasing.
flowchart TD GD["Gradient<br/>Descent"] -->|minimises| CF["Cost<br/>function"] GD -->|updates| W["Parameters<br/>(weights)"] GD -->|uses| GR["Gradient<br/>(slope)"] GD --> H["Hyperparameters"] H --> LR["Learning<br/>rate"] H --> BS["Batch<br/>size"] H --> EP["Epochs"] style GD fill:#90CAF9,stroke:#1E88E5,color:#000 style CF fill:#CE93D8,stroke:#8E24AA,color:#000 style W fill:#CE93D8,stroke:#8E24AA,color:#000 style GR fill:#CE93D8,stroke:#8E24AA,color:#000 style H fill:#CE93D8,stroke:#8E24AA,color:#000 style LR fill:#CE93D8,stroke:#8E24AA,color:#000 style BS fill:#CE93D8,stroke:#8E24AA,color:#000 style EP fill:#CE93D8,stroke:#8E24AA,color:#000
flowchart TD T["Gradient Descent<br/>types"] --> BGD["Batch<br/>GD"] T --> SGD["Stochastic<br/>GD"] T --> MGD["Mini-batch<br/>GD"] BGD --> ALL["All data<br/>per step"] BGD --> STB["Smooth<br/>updates"] SGD --> ONE["1 sample<br/>per step"] SGD --> FAST["Quick<br/>progress"] SGD --> NOISE["Noisy<br/>updates"] MGD --> MB["Small batch<br/>per step"] MGD --> PRACT["Practical<br/>default"] style T fill:#90CAF9,stroke:#1E88E5,color:#000 style BGD fill:#C8E6C9,stroke:#2E7D32,color:#000 style SGD fill:#C8E6C9,stroke:#2E7D32,color:#000 style MGD fill:#C8E6C9,stroke:#2E7D32,color:#000 style ALL fill:#CE93D8,stroke:#8E24AA,color:#000 style STB fill:#CE93D8,stroke:#8E24AA,color:#000 style ONE fill:#CE93D8,stroke:#8E24AA,color:#000 style FAST fill:#CE93D8,stroke:#8E24AA,color:#000 style NOISE fill:#CE93D8,stroke:#8E24AA,color:#000 style MB fill:#CE93D8,stroke:#8E24AA,color:#000 style PRACT fill:#CE93D8,stroke:#8E24AA,color:#000
go-to solution
March 12, 2026Hypothesis testing is a structured way to decide:
Is what we see in a sample just random variation, or is there evidence of a real effect in the population?
Hypothesis Testing topic sits inside inferential statistics: we use a sample to make a statement about a population.
Key takeaway: The logic is always the same:
February 15, 2026A Linear Neural Network (LNN) for classification uses no hidden layers.
It learns a linear decision boundary and outputs class probabilities, then converts them into predicted classes.
Neural-network view:
- Binary classification → logistic regression (single neuron + sigmoid)
- Multi-class classification → softmax regression (K output neurons + softmax)
flowchart LR D["Data<br/>X, y"] --> M["Linear model<br/>w, b"] M --> A["Activation<br/>Sigmoid / Softmax"] A --> L["Loss<br/>Cross-entropy"] L --> O["Optimiser<br/>Mini-batch GD / Adam"] O --> P["Updated parameters<br/>w, b"] P --> I["Inference<br/>Probabilities → class"] %% Pastel colour scheme style D fill:#E3F2FD,stroke:#1E88E5,stroke-width:1px style M fill:#E8F5E9,stroke:#43A047,stroke-width:1px style A fill:#FFF3E0,stroke:#FB8C00,stroke-width:1px style L fill:#FCE4EC,stroke:#D81B60,stroke-width:1px style O fill:#F3E5F5,stroke:#8E24AA,stroke-width:1px style P fill:#E0F7FA,stroke:#00838F,stroke-width:1px style I fill:#F1F8E9,stroke:#558B2F,stroke-width:1px
Classification predicts a discrete class label.
Common settings:
flowchart TD T["Linear<br/>classification<br/>models"] --> P["Perceptron"] T --> LR["Logistic<br/>regression"] T --> SVM["Linear<br/>SVM"] P -->|uses| STEP["Step<br/>activation"] LR -->|uses| SIG["Sigmoid<br/>+ log loss"] SVM -->|uses| HNG["Hinge<br/>loss"] style T fill:#90CAF9,stroke:#1E88E5,color:#000 style P fill:#C8E6C9,stroke:#2E7D32,color:#000 style LR fill:#C8E6C9,stroke:#2E7D32,color:#000 style SVM fill:#C8E6C9,stroke:#2E7D32,color:#000 style STEP fill:#CE93D8,stroke:#8E24AA,color:#000 style SIG fill:#CE93D8,stroke:#8E24AA,color:#000 style HNG fill:#CE93D8,stroke:#8E24AA,color:#000
Key takeaway: Logistic regression predicts $P(y=1\mid x)$ using a sigmoid of a linear score $z=w\cdot x+b$, then learns $w,b$ by maximising likelihood (equivalently minimising log-loss).
December 14, 2025AI 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
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:
LLMs are based on the Transformer Architecture, which allows models to understand context and long-range dependencies in text.
December 15, 2025Also 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) is a system design pattern that improves an LLM’s answers by:
RAG helps an LLM look things up first, then answer using evidence.
RAG is commonly used when:
RAG does not change the model weights.
It changes what the model sees at inference time by adding retrieved context.
March 18, 2026Machine Learning is built on mathematical principles that allow models to:
flowchart LR
DATA[Data]
MATH[Math Models]
OPT[Optimisation]
MODEL[Trained Model]
DATA --> MATH
MATH --> OPT
OPT --> MODEL
ML requires core mathematical tools to understand how ML algorithms work internally. Algebra deals with relationships between variables and quantities, while Calculus focuses on change and optimization.