Computer vision
Models that see: classification, detection, segmentation and their limits.
14tutors
5teachers
1free to start
$4 to $12per paid lesson
Computer vision tutors
14 tutors
Object detection explained
Object detection explained
Understand boxes, IoU, NMS and mAP well enough to train, evaluate and debug a detector75 lessonsSampleNoor Siddiqui$8Image embeddings and visual searchImage embeddings and visual search
Build image similarity and text to image search, and measure whether results are actually relevant71 lessonsSampleNoor Siddiqui$7Image classification, first principlesImage classification, first principles
Plan, train and honestly evaluate an image classifier, from defining classes to studying its mistakes64 lessonsSampleNoor SiddiquiFreeVision language models: what they seeVision language models: what they see
Know how AI models read images, what they get right and wrong, and how to check their answers61 lessonsSampleNoor Siddiqui$6Convolutional networks from the pixel upConvolutional networks from the pixel up
See how convolutions turn pixels into features, and calculate shapes and parameters yourself56 lessonsSampleNikolai Sorin$5OCR and document understandingOCR and document understanding
Understand how machines read scans, forms and tables, and how to check that they read correctly51 lessonsSampleNoor Siddiqui$5Transfer learning with pretrained modelsTransfer learning with pretrained models
Get strong results from small datasets by starting with a model that has already learned50 lessonsSampleMateo Rojas$5Data augmentation for visionData augmentation for vision
Choose image augmentations that reflect real variation, keep labels correct and improve generalisation44 lessonsSampleNoor Siddiqui$4Image segmentation: semantic, instance, panopticImage segmentation: semantic, instance, panoptic
Label every pixel correctly: understand the three kinds of segmentation, their models and metrics40 lessonsSampleNoor Siddiqui$8Medical imaging AI: how it is built and checkedMedical imaging AI: how it is built and checked
Understand how imaging models are trained, validated and overseen, for education onlyMalik Brennan$8AI visual inspection for factories, explainedAI visual inspection for factories, explained
Learn how camera based defect detection works on a production line, and what makes it succeedMagnus Eriksen$5Self supervised and contrastive learningSelf supervised and contrastive learning
Learn how models build useful representations from unlabelled data, and how to test themMateo Rojas$11Teachers who teach Computer vision
They wrote the lesson plans these tutors follow.
Noor Siddiqui
Computer vision taught through real images, real failure cases and careful evaluationimage classification, object detection, segmentation9 tutorsSampleNikolai Sorin
Architectures explained from the inside: convolutions, recurrence, attention and beyondconvolutional networks, recurrent networks, transformers9 tutorsSampleMagnus Eriksen
Making models fast, small and affordable: hardware, quantisation, serving and edgeGPUs and accelerators, training cost estimation, quantisation9 tutorsSampleMateo Rojas
Generative models and representation learning, explained with intuition first and maths secondtransfer learning, autoencoders, GANs9 tutorsSampleMalik Brennan
MLOps without the ceremony: tracking, versioning, monitoring and responsible deploymentexperiment tracking, model and data versioning, model APIs9 tutorsSampleMore in Machine learning
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