# Neural Networks: Zero to Hero Source: https://freeaicourses.com/courses/neural-networks-zero-to-hero Last checked: 2026-10-05 (teacher, video list and durations, prerequisites, notebooks, cost, certificate and series status) ## Short answer Our Karpathy Neural Networks Zero to Hero review: Neural Networks: Zero to Hero is the best free course for coders who want to see how a language model works by building one. Andrej Karpathy teaches 8 videos, about 14.5 hours in total, from backpropagation to a GPT and its tokeniser. It costs nothing, and there is no certificate. ## Key facts - Teacher: Andrej Karpathy - Length: 8 videos, 872 minutes (about 14.5 hours of video) - Prerequisite: Solid Python and intro-level maths - Cost: Free on YouTube - Certificate: None - Notebooks: Videos link Google Colab notebooks - Status: Ongoing: the syllabus ends with "ongoing..." - Community: Discord channel ## Who it is for - Coders who want to build a GPT from scratch and see how each part works - Learners with solid Python and intro-level maths, such as derivatives and the Gaussian - Anyone who has finished a first deep learning course and wants the ideas underneath ## Who should skip it - Anyone new to Python: the course asks for solid programming - Learners who want a certificate: the course offers none - Anyone who wants quizzes and graded work: only the micrograd video mentions an exercise ## What you can do afterwards - Build backpropagation from scratch by writing micrograd - Build character-level language models, from a bigram model to a WaveNet - Explain activations, gradients and BatchNorm, and backpropagate by hand without PyTorch autograd - Build a GPT from scratch, following "Attention is All You Need" and GPT-2 and GPT-3 - Build a GPT tokeniser using byte pair encoding ## What the course covers ### Video 1: Intro to neural networks and backpropagation Build micrograd, a small backpropagation engine, from scratch. 2 hours 25 minutes. ### Video 2: Intro to language modelling Build makemore, a bigram character-level language model, covering torch.Tensor, training, sampling and loss. 1 hour 57 minutes. ### Video 3: Makemore part 2, MLP A multilayer perceptron model, with learning rate tuning, hyperparameters, train, dev and test splits, and under and overfitting. 1 hour 15 minutes. ### Video 4: Makemore part 3, activations and gradients Activations, gradients and BatchNorm. 1 hour 55 minutes. ### Video 5: Makemore part 4, becoming a backprop ninja Backpropagate by hand, without PyTorch autograd. 1 hour 55 minutes. ### Video 6: Makemore part 5, building a WaveNet A deeper, tree-like convolutional model, using torch.nn. 56 minutes. ### Video 7: Let's build GPT Build GPT from scratch in code, following "Attention is All You Need" and GPT-2 and GPT-3. 1 hour 56 minutes. ### Video 8: Let's build the GPT tokeniser Byte pair encoding, encoding and decoding, and why many odd LLM behaviours trace back to tokenisation. 2 hours 13 minutes. ## What works - It costs nothing: all 8 videos are free on YouTube - You build everything in code, starting with backpropagation and ending with a GPT - The videos link Google Colab notebooks, so you need no hardware of your own - It focuses on language models, and most of what you learn transfers to other areas such as computer vision - The first video assumes only basic Python and a vague memory of high school calculus ## What does not - There is no certificate - There are no quizzes or graded work: only the micrograd video mentions an exercise, a Colab notebook - The real time is well above 14.5 hours, because you code along and pause to think - The later videos expect solid Python, and the course asks for intro-level maths - The series is marked as ongoing, so the video list may grow ## The catch There is no certificate, and there are no quizzes or graded work: only the micrograd video points you to an exercise. The 8 videos run about 14.5 hours, but the real time is well above that, because you are meant to code along. The syllabus ends with "ongoing", so Karpathy may add videos. ## Is it still current? The course page was checked on 5 October 2026. It lists 8 videos totalling 872 minutes, and its syllabus ends with "ongoing...", so Karpathy may add more. It describes the course as building neural networks from scratch, in code, from the basics of backpropagation up to modern networks like GPT. ## Take before - Practical Deep Learning for Coders - Hugging Face LLM Course ## Take after AI Agents Course ## Verdict Take Neural Networks: Zero to Hero if you are comfortable in Python and want to understand what is inside a language model by writing it yourself. It scores 9.3: hands-on and depth are 10 because you build everything from scratch, and truly free is 10, while clarity is 9 and up to date is 8. It offers no certificate and almost no graded work, so you set your own exercises. ## Questions people ask ### Is Neural Networks: Zero to Hero free? Yes. The videos are free on YouTube, and there is no certificate. Karpathy also invites learners to join the course's Discord channel. ### Is Neural Networks: Zero to Hero good for beginners? Not for beginners to coding. The course asks for solid Python and intro-level maths. The first video assumes basic Python and a vague memory of high school calculus. ### What maths do you need for Zero to Hero? Intro-level maths, such as derivatives and the Gaussian. The micrograd video assumes only a vague recollection of calculus from high school. ### How long does Zero to Hero take? There are 8 videos totalling 872 minutes, about 14.5 hours. This site lists 14.5 hours, which is video time only, so the real time is well above that if you code along. ### Do you need a GPU for Zero to Hero? No special hardware is needed to start. The videos link Google Colab notebooks, and the GPT video's model was trained on a cloud GPU. ### What should you take before Zero to Hero? Take Practical Deep Learning for Coders or the Hugging Face LLM Course first if you want a deep learning course behind you. You need solid Python either way. ## Related pages - [AI Agents Course](https://freeaicourses.com/courses/hugging-face-agents-course) - [Practical Deep Learning for Coders](https://freeaicourses.com/courses/practical-deep-learning-for-coders) - [Hugging Face LLM Course](https://freeaicourses.com/courses/hugging-face-llm-course) - [Deep learning, hands-on](https://freeaicourses.com/paths/deep-learning) - [Build apps with LLMs](https://freeaicourses.com/paths/build-with-llms) - [All free AI courses, rated](https://freeaicourses.com/courses)