Deep learning
Neural networks from single neurons to full training loops, with worked examples.
28tutors
6teachers
2free to start
$4 to $13per paid lesson
Deep learning tutors
28 tutors
Backpropagation in practice
Backpropagation in practice
Follow gradients through a real network and fix the training bugs that come from misusing them91 lessonsSampleMira Okafor$7Deep learning without the jargonDeep learning without the jargon
Understand what deep learning is, what it does well and where it fails, with no maths needed78 lessonsSampleMira OkaforFreeAttention mechanisms, step by stepAttention mechanisms, step by step
Compute attention by hand, then understand masks, heads, KV caching and efficient variants66 lessonsSampleNikolai Sorin$8Debugging neural network trainingDebugging neural network training
A systematic method for finding why a model will not train, diverges or quietly underperforms66 lessonsSampleNikolai Sorin$10The transformer, block by blockThe transformer, block by block
Trace a token through every part of a transformer and count where the parameters live60 lessonsSampleNikolai Sorin$12Optimisers: SGD, momentum and AdamOptimisers: SGD, momentum and Adam
Choose and tune optimisers and learning rate schedules with understanding instead of guesswork56 lessonsSampleMira Okafor$8Convolutional 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$5Loss functions: what your model is minimisingLoss functions: what your model is minimising
Understand MSE, cross entropy and friends well enough to choose, read and debug them53 lessonsSampleMira Okafor$5GANs: generator versus discriminatorGANs: generator versus discriminator
Understand how adversarial training works, why it is unstable and where GANs still make sense50 lessonsSampleMateo Rojas$8Transfer 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$5Activation functions explainedActivation functions explained
Learn what ReLU, sigmoid, tanh, GELU and softmax do, and choose the right one for each layer50 lessonsSampleMira Okafor$4Overfitting, regularisation and dropoutOverfitting, regularisation and dropout
Recognise overfitting from your curves and pick the right fix, from more data to dropout50 lessonsSampleMira Okafor$5Teachers who teach Deep learning
They wrote the lesson plans these tutors follow.
Neha Varadan
Fine tuning with judgment: when to do it, how to do it well, and how to know it workedfine tuning strategy, dataset preparation, LoRA and parameter efficient methods9 tutorsSampleMira Okafor
I teach how neural networks learn, one small worked example at a timeneural network fundamentals, activation and loss functions, backpropagation9 tutorsSampleNoor 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 tutorsSampleMore in Machine learning
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