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Foundations 89

All Foundations89 AI basics25 How language models work23 AI safety and ethics49

Everyday AI 91

All Everyday AI91 Prompting20 ChatGPT and Claude at work25 AI for writing29 AI for research and study33

Building with AI 162

All Building with AI162 Python for AI21 Building with LLM APIs30 RAG and search21 Evaluation and testing21 AI agents31 AI automation25 AI coding assistants30

Machine learning 149

All Machine learning149 Machine learning35 Data science and statistics30 Math for AI23 Deep learning28 NLP14 Computer vision14 Fine tuning and training12 MLOps and deployment18

Creative AI 77

All Creative AI77 Image and video AI58 Audio and voice AI12 AI for design33

AI at work 145

All AI at work145 AI for business34 AI for marketing and sales23 AI for product and operations28 AI for education28 AI for professionals29 AI careers18

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Deep learning Clear all

Backpropagation in practice

Backpropagation in practice

Follow gradients through a real network and fix the training bugs that come from misusing themIntermediateDeep learning4.5(4)91 lessonsSampleMira Okafor$7 Deep learning without the jargon

Deep learning without the jargon

Understand what deep learning is, what it does well and where it fails, with no maths neededBeginnerAI basics4.7(3)78 lessonsSampleMira OkaforFree Attention mechanisms, step by step

Attention mechanisms, step by step

Compute attention by hand, then understand masks, heads, KV caching and efficient variantsIntermediateDeep learning4.3(4)66 lessonsSampleNikolai Sorin$8 Debugging neural network training

Debugging neural network training

A systematic method for finding why a model will not train, diverges or quietly underperformsAdvancedDeep learning4.5(4)66 lessonsSampleNikolai Sorin$10 The transformer, block by block

The transformer, block by block

Trace a token through every part of a transformer and count where the parameters liveAdvancedDeep learning4.7(3)60 lessonsSampleNikolai Sorin$12 Optimisers: SGD, momentum and Adam

Optimisers: SGD, momentum and Adam

Choose and tune optimisers and learning rate schedules with understanding instead of guessworkIntermediateDeep learning4.7(3)56 lessonsSampleMira Okafor$8 Convolutional networks from the pixel up

Convolutional networks from the pixel up

See how convolutions turn pixels into features, and calculate shapes and parameters yourselfBeginnerComputer vision4.7(3)56 lessonsSampleNikolai Sorin$5 Loss functions: what your model is minimising

Loss functions: what your model is minimising

Understand MSE, cross entropy and friends well enough to choose, read and debug themBeginnerDeep learning4.0(3)53 lessonsSampleMira Okafor$5 GANs: generator versus discriminator

GANs: generator versus discriminator

Understand how adversarial training works, why it is unstable and where GANs still make senseIntermediateDeep learning4.0(3)50 lessonsSampleMateo Rojas$8 Transfer learning with pretrained models

Transfer learning with pretrained models

Get strong results from small datasets by starting with a model that has already learnedBeginnerComputer vision4.7(3)50 lessonsSampleMateo Rojas$5 Activation functions explained

Activation functions explained

Learn what ReLU, sigmoid, tanh, GELU and softmax do, and choose the right one for each layerBeginnerDeep learning4.5(2)50 lessonsSampleMira Okafor$4 Overfitting, regularisation and dropout

Overfitting, regularisation and dropout

Recognise overfitting from your curves and pick the right fix, from more data to dropoutBeginnerDeep learning4.7(3)50 lessonsSampleMira Okafor$5