Skip to content

SamplePreview build: teacher profiles, ratings, reviews and lesson counts are sample data.

Log in Find a tutor

Find a tutor

657 tutors in 31 topics, built by 75 teachers. Each one follows a lesson plan its teacher wrote.

Filters

Sort by

Price

Any price Free Paid

Level

Any level Beginner Intermediate Advanced

Topic

All topics

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

Clear

12 tutors

Deep learning Beginner Clear all

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 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 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 Neurons and layers, built up by hand

Neurons and layers, built up by hand

Compute a small neural network on paper so every layer, weight and shape makes senseBeginnerDeep learning4.7(3)46 lessonsSampleMira Okafor$4 Autoencoders and latent spaces

Autoencoders and latent spaces

Learn how networks compress data into a small code and rebuild it, and what that code is good forBeginnerDeep learning4.5(2)44 lessonsSampleMateo Rojas$5 Data augmentation for vision

Data augmentation for vision

Choose image augmentations that reflect real variation, keep labels correct and improve generalisationBeginnerComputer vision4.0(3)44 lessonsSampleNoor Siddiqui$4 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 Reinforcement learning basics

Reinforcement learning basics

Understand agents, rewards and policies, and compute a Q learning update yourselfBeginnerDeep learning4.7(3)41 lessonsSampleNikolai SorinFree Writing your first training loop

Writing your first training loop

Write a clear, correct training loop in any framework and know what each line is forBeginnerDeep learningNewMira Okafor$4