4.4SpecializationSession 3 · Deep Learning & Applied Machine Learning

Deep learning algorithms

30classes90htotalTH3 · PR3 · PNW2weighting per class

Class-by-class breakdown

30 classes

TH = theory · PR = practical · PNW = personal work. These are ministry weighting codes (not hours) used to split each class's minutes. Class length = course hours ÷ class count; the three time-boxes below always sum to that length.

Quiz progress

0 of 0 classes attempted

First 2 classes free with a referral 29 more with enrollment180 min per classTheory 68mPractical 67mPersonal Work 45m

From ML to Deep Learning — The Engine Behind GenAI

FreeTH3PR3PNW2

An AI Integration Manager who knows when deep learning beats traditional ML picks the right tool — and understands that the GenAI revolution runs on deep neural networks.

Theory68m

Recap of supervised ML from 3.4 and its limits
What deep learning adds: many layers, learned features
Where deep learning powers GenAI (text, images, audio) vs where it's overkill

Practical67m

Classify 8 sample GenAI tasks as 'traditional ML enough' or 'needs deep learning'
Justify one choice with data and complexity reasoning

Personal Work45m

Write a one-page note on when you'd reach for deep learning in a GenAI project
Micro-task: submit your note

The Perceptron — A Single Neuron

LockedTH3PR3PNW2

Locked class preview

A Data/Business Analyst who understands one neuron understands the building block of every transformer — it's just a weighted vote scaled by an activation

Enroll to unlock every class in this course — the full agenda, the AI tutor, and the quiz.

Deep learning algorithms

Class-by-class breakdown

From ML to Deep Learning — The Engine Behind GenAI

The Perceptron — A Single Neuron

Activation Functions — Letting Networks Be Non-Linear

Multi-Layer Networks — Stacking Toward Transformers

Loss and Gradients — How GenAI Models Learn

Backpropagation — The Chain Rule That Trained Every LLM

Your First Neural Network in PyTorch (or TensorFlow)

Tensors, Batches and Dataloaders — Data for Deep Models

Optimizers — SGD, Momentum and Adam

Overfitting and Regularization — Dropout, Weight Decay and Early Stopping

Evaluating Deep Models — Beyond Accuracy

Computer Vision 101 — Images as Tensors

CNNs — Vision Models for Multimodal Agents

Building a CNN — Your First Image Classifier

Data Augmentation — More Data From What You Have

Transfer Learning — Standing on Giant Shoulders

Sequences and Text — Why GenAI Needed Sequence Models

RNNs and LSTMs — Memory for Longer Sequences

Embeddings — The Vector Heart of RAG and Agents

Attention — The Mechanism That Powers Every LLM

Transformers — The Architecture Behind GenAI and Agents

Using Pre-Trained Transformer Models via Hugging Face

Fine-Tuning a Pre-Trained Model for Your Business

LLM Tool-Calling as a Learned Policy — Agents That Act

RLHF and Alignment — Making GenAI Safe and Helpful

Deploying a Deep Learning Model as an Agent Tool

Responsible GenAI — Bias, Cost and Limits

Hyperparameter Tuning — Getting the Most From a Model

Mini-Project — Build, Tune and Ship a Model for an Agent

Present and Reflect — Your Deep Learning to GenAI Journey

Unlock all 30 classes