AI, Machine Learning and Deep Learning
See how the three ideas fit together.
Read lessonStep-by-step learning
Deep Learning, one idea at a time
Start with AI and neural networks. Then learn how models train, read language, understand images and make decisions. Each lesson has a simple explanation, a visual flow and an example.
Your learning path
Follow the lessons in order, or jump to a topic you want to revise. Terms are written in full on their first use.
Foundations
Perceptron, Weights and Bias
Meet the basic calculation inside a neural network.
Read lessonActivation Functions and Nonlinearity
Learn why a network needs more than straight lines.
Read lessonNeural Networks and Hidden Layers
Follow information from inputs to a prediction.
Read lessonLoss, Error and Backpropagation
See how a network learns from a wrong answer.
Read lessonGradient Descent and Learning Rate
Understand the small steps used to improve a model.
Read lessonEpochs, Batches and Training Cycles
Learn what happens when a model sees examples repeatedly.
Read lessonTraining, Validation and Test Data
Give each set of examples a different job.
Read lessonOverfitting, Underfitting and Early Stopping
Know when a model has learned too little or memorised too much.
Read lessonAccuracy, Precision, Recall and F1 Score
Check what “good performance” actually means.
Read lessonConfusion Matrix Explained
Put correct and incorrect predictions into four boxes.
Read lessonLabels, Supervised and Unsupervised Learning
Understand what answer the model is given while learning.
Read lessonANN Lab: Training, Inference and Tuning
Connect classroom concepts to a simple model workflow.
Read lessonCPU, GPU and TPU for Deep Learning
See why large neural networks need computing power.
Read lessonDeep Learning for Business Decisions
Choose a useful problem and handle predictions carefully.
Read lessonWhen to Use Rules, ML or Deep Learning
Choose a method that fits the question and the available data.
Read lessonWhy Deep Learning Needs Data and Pre-training
Understand what a deep model learns from examples.
Read lessonLanguage
Natural Language Processing Basics
Help a computer work with human language.
Read lessonTokenization and Vocabulary
Turn text into pieces a model can process.
Read lessonBag of Words and TF-IDF
Compare two simple ways to represent text.
Read lessonWord Embeddings Explained
Represent meaning with groups of numbers.
Read lessonRNN and Sequence Data
Read information when order matters.
Read lessonLSTM and Long-Term Context
See how an RNN can keep important information longer.
Read lessonAutoencoders and Anomaly Detection
Compress data, rebuild it, and notice unusual cases.
Read lessonAttention and Self-Attention
Focus on the parts of a sentence that matter.
Read lessonTransformers, BERT and GPT
Understand the main architecture behind modern language models.
Read lessonSentiment Analysis and the NLP Lab
Walk through a simple text classification exercise.
Read lessonVision
Computer Vision and Pixels
Understand the numbers inside a digital image.
Read lessonConvolutional Neural Networks
See why CNNs are useful for images.
Read lessonFilters, Kernels and Feature Maps
Follow a small filter across an image.
Read lessonPooling, Flattening and Classification
Turn visual features into a final answer.
Read lessonLeNet and Handwritten Digits
Meet an early practical CNN.
Read lessonAlexNet, ReLU and Dropout
See why deeper image models became practical.
Read lessonVGG and Small Filters
Learn the simple design idea behind VGG.
Read lessonInception and Multiple Filter Sizes
Compare patterns at more than one scale.
Read lessonResNet and Skip Connections
Understand the shortcut that helps deep networks learn.
Read lessonEfficientNet: Depth, Width and Resolution
Balance model size and image detail.
Read lessonObject Detection, Bounding Boxes and R-CNN
Find what is in a picture and where it is.
Read lessonYOLO and Real-Time Object Detection
Learn a fast way to find objects in images and video.
Read lessonGANs and Synthetic Images
Understand a generator and a discriminator.
Read lessonBeyond images
CNN and RNN Together for Video
Combine what appears in a frame with what happens over time.
Read lessonDeep Learning for Audio and Sensor Data
Apply pattern learning beyond text and pictures.
Read lessonReinforcement Learning and Rewards
Learn by trying actions and receiving feedback.
Read lessonReasoning Architectures in AI
Break a task into steps and check the result.
Read lessonPractice and assessment
Computer Vision Lab: A Simple Workflow
See how a small image model is built and checked.
Read lessonDeep Learning Cohort Presentation Guide
Explain your model clearly in a short assessment.
Read lessonFind lessons by session
The course outline groups several small ideas into each session. Use this map to find the starting lesson for that session.
Session 1From Biology to PerceptronsSession 2Nonlinearity and multilayer networksSession 3Backpropagation and gradient descentSession 4ANN labSession 5Introduction to Deep LearningSession 6NLP: autoencoders, embeddings, RNN and LSTMSession 7NLP: attention and TransformersSession 8NLP labSession 9CNN basicsSession 10LeNet and AlexNetSession 11VGG to EfficientNetSession 12R-CNN, YOLO and GANsSession 13Other media, RL and reasoningSession 14Cohort presentationSession 15Presentation and CV lab