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Deep Learning Lab

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Guide

Full curriculum

Module overview
1.1 Where Machine Learning Runs Out of Road
1.2 The Learning Mechanism, Traced By Hand
1.3 Your First PyTorch Network

Module overview
2.1 Tensors and the Shape Contract
2.2 Dataset and DataLoader: Feeding the Model
2.3 Building and Training an MLP You Can Defend
2.4 From Notebook to Project

Module overview
3.1 Optimizers and the Learning Rate
3.2 Overfitting, and What to Do About It
3.3 When Gradients Misbehave
3.4 The Diagnosis Clinic

Module overview
4.1 Data You Can Trust
4.2 Experiments You Can Reproduce
4.3 Memory, Batch Size and Precision
4.4 The Bake-Off: Gradient Boosting against a Neural Network

Module overview
5.1 Understanding Representation Learning
5.2 How Neural Networks Learn from Images
5.3 How Neural Networks Learn from Text
5.4 From Sequences to Attention
5.5 Connecting Image and Text Representations
5.6 Synthesis: Putting Representation Learning into Practice

Module overview
6.1 Understanding the Transformer Architecture
6.2 Preparing Inputs for a Transformer
6.3 Understanding Attention
6.4 The Transformer Block
6.5 Building a Decoder-Only Transformer
6.6 How Transformers Learn and Generate Text

Module overview
7.1 Understanding Pretrained Models
7.2 The Hugging Face Ecosystem (Essentials)
7.3 Finding and Selecting the Right Model
7.4 Loading and Using Pretrained Models
7.5 Building Applications with Pretrained Models
7.6 Inference and Deployment Constraints
7.7 Practical Model Selection and Integration

Module overview
8.1 Understanding Model Adaptation
8.2 Preparing Data for Fine-Tuning
8.3 Establishing Baselines
8.4 The Fine-Tuning Workflow
8.5 Parameter-Efficient Fine-Tuning (PEFT)
8.6 Fine-Tuning Large Language Models (Conceptual)
8.7 Evaluating Adapted Models

9 Evaluation, Inference & Model Economics

10 Keras 3 / TensorFlow & Model Engineering

My progress Daily review Concept map Glossary How to use this platform

Daily review

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Tap an answer. You will see why straight after.

Keys: A–D to answer, ↵ for the next question, Esc to stop. Stopping keeps what you answered.

Practise a lesson

Pick a lesson and answer ten of its checkpoint and quiz questions in a random order. Each answer joins your review deck.

Lesson

Daily review

–due today

Due by lesson

How spacing works A missed question comes back tomorrow. Each right answer sends it further out: a few days, a week, two weeks, a month, then three months.

Deep Learning Lab

Built to be understood. Designed to be tried.

Course introduction How to use the lab Solomon Promise Instructor view Source course

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