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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 Intermediate 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 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 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 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 How to read a deep learning paper

How to read a deep learning paper

Read papers in passes, find the real claim and judge the evidence behind itAll levelsAI for research and study4.7(3)41 lessonsSampleNikolai Sorin$6 RNNs, LSTMs and why transformers took over

RNNs, LSTMs and why transformers took over

Understand recurrent networks, their gates and limits, and the real reasons attention replaced themIntermediateDeep learning4.5(2)32 lessonsSampleNikolai Sorin$7 Positional information in transformers

Positional information in transformers

Learn how transformers know word order, from sinusoids to rotary embeddings and long context limitsIntermediateDeep learningNewNikolai Sorin$8