Module 4 Additional Resources — ML & DL Foundations

Contents

Module 4 Additional Resources — ML & DL Foundations#

Video Resource#

Hiker in the Fog — ML Analogy Video — Visual explanation of how machine learning works

Companion Resources (from this module)#

Hiker’s Cheat Sheet — Rosetta Stone mapping the analogy terms to technical ML/DL terms
Knowledge Checks — 5 questions to confirm your understanding (answer in plain English)

Conceptual Foundations#

Michael Nielsen — Neural Networks and Deep Learning (Chapters 1–2)
Andrew Ng — ML course (focus: loss, overfitting, bias/variance)

The Big Two Frameworks#

PyTorch tutorials: https://pytorch.org/tutorials/
TensorFlow/Keras quickstart: https://www.tensorflow.org/guide/keras

Practical Notes for Financial Services#

Data quality and bias shape model behaviour
Separate training vs inference responsibilities
Validate, monitor, and govern models like any other critical system

Bridge to next modules#

Embeddings + vector search enable retrieval
RAG grounds LLM outputs with evidence to reduce hallucination risk