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What is deeplearningzerotoall?

hunkim/deeplearningzerotoall — explained in plain English

Analysis updated 2026-06-26

4,498Jupyter NotebookAudience · researcherComplexity · 2/5Setup · moderate

In one sentence

A set of numbered lab code files for learning deep learning basics with TensorFlow, Keras, and MXNet, companion code to a Korean-language YouTube tutorial series, written to be easy to read rather than fast to run.

Mindmap

mindmap
  root((DeepLearning ZeroToAll))
    What it does
      Deep learning labs
      YouTube series companion
      Beginner-focused code
    Frameworks
      TensorFlow labs
      Keras labs
      MXNet labs
    Format
      Numbered lab files
      Readable teaching style
      Jupyter notebooks
    Audience
      ML beginners
      Korean learners
      Students
    Topics
      Neural network basics
      Model training
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Code map

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What do people build with it?

USE CASE 1

Follow along with the Korean YouTube deep learning lecture series using the matching numbered lab code files.

USE CASE 2

Learn TensorFlow basics by running lab notebooks in sequence, each focused on a single concept.

USE CASE 3

Study Keras or MXNet as alternatives to TensorFlow using the parallel klab- and mxlab- files.

What is it built with?

PythonTensorFlowKerasMXNetJupyter Notebook

How does it compare?

hunkim/deeplearningzerotoallgoogle-research/simclrtingsongyu/pytorch-tutorial-2nd
Stars4,4984,4924,506
LanguageJupyter NotebookJupyter NotebookJupyter Notebook
Setup difficultymoderatehardmoderate
Complexity2/55/53/5
Audienceresearcherresearcherdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

TensorFlow version requirements are not specified, labs are designed to follow the YouTube series, which is in Korean.

No license information is specified in the repository.

So what is it?

DeepLearningZeroToAll is a collection of lab code files that accompany a deep learning tutorial series originally delivered in Korean and published on YouTube. The code is intended to help people learn the basics of building and training machine learning models using TensorFlow, with some labs also covering Keras and MXNet (two other tools in the same space). The files are numbered and named by topic, with a prefix indicating which framework they use: files starting with lab- are TensorFlow labs, klab- files are Keras labs, and mxlab- files are MXNet labs. The style throughout prioritizes being easy to read and understand over being computationally efficient, because the primary purpose is teaching, not production use. The README describes the project as a work in progress and is sparse on details about which specific topics each lab covers. Slide decks for the lectures are linked externally. The project accepts contributions and comments, and was intended at the time of writing to eventually produce an English version of the video series as well. This repository is most useful if you are following along with the original YouTube lecture series, since the lab files are keyed to those videos. As a standalone code collection, the README does not describe the full scope of what is covered, so someone arriving without that context would need to browse the individual files to understand what each one does. The license and specific TensorFlow version requirements are not stated in the README.

Copy-paste prompts

Prompt 1
I'm following the hunkim/deeplearningzerotoall YouTube series. How do I set up a Python environment, install TensorFlow, and run the first lab notebook from the repository?
Prompt 2
Looking at the TensorFlow lab files in hunkim/deeplearningzerotoall, walk me through what lab-09 covers and explain the key parts of the code.
Prompt 3
Compare the TensorFlow and Keras versions of the same deep learning concept in hunkim/deeplearningzerotoall. Pick a topic that has both a lab- and a klab- file and explain the differences.
Prompt 4
Using the MXNet lab files in hunkim/deeplearningzerotoall as a reference, show me how to build and train a simple neural network in MXNet to classify handwritten digits.

Frequently asked questions

What is deeplearningzerotoall?

A set of numbered lab code files for learning deep learning basics with TensorFlow, Keras, and MXNet, companion code to a Korean-language YouTube tutorial series, written to be easy to read rather than fast to run.

What language is deeplearningzerotoall written in?

Mainly Jupyter Notebook. The stack also includes Python, TensorFlow, Keras.

What license does deeplearningzerotoall use?

No license information is specified in the repository.

How hard is deeplearningzerotoall to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is deeplearningzerotoall for?

Mainly researcher.

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