What is tensorflow-deep-learning?

mrdbourke/tensorflow-deep-learning — explained in plain English

Analysis updated 2026-06-26

★ 5,903Jupyter NotebookAudience · developerComplexity · 3/5Setup · moderate

Mindmap

mindmap
  root((tensorflow-deep-learning))
    What it does
      Full course materials
      Jupyter notebooks
      Progressive curriculum
    Topics Covered
      Neural network basics
      Computer vision
      Natural language processing
      Transfer learning
    Projects
      Food image classifier
      Paper skimming tool
    Access
      First 14h free on YouTube
      Paid academy content
      Free online book

mindmap root((tensorflow-deep-learning)) What it does Full course materials Jupyter notebooks Progressive curriculum Topics Covered Neural network basics Computer vision Natural language processing Transfer learning Projects Food image classifier Paper skimming tool Access First 14h free on YouTube Paid academy content Free online book

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What do people build with it?

USE CASE 1

Work through structured notebooks to learn how to build neural networks for image classification, NLP, and regression with TensorFlow.

USE CASE 2

Build a food image classifier as a milestone project, learning how to apply transfer learning with a pretrained model.

USE CASE 3

Run Jupyter notebooks locally to follow along with the Zero to Mastery TensorFlow course and complete the provided exercises.

USE CASE 4

Reference the fixes section to resolve TensorFlow API compatibility issues when running older course notebooks on newer TensorFlow versions.

What is it built with?

PythonTensorFlowKerasJupyter Notebook

How does it compare?

	mrdbourke/tensorflow-deep-learning	ofa-sys/chinese-clip	snakers4/silero-models
Stars	5,903	5,900	5,917
Language	Jupyter Notebook	Jupyter Notebook	Jupyter Notebook
Setup difficulty	moderate	moderate	easy
Complexity	3/5	3/5	2/5
Audience	developer	researcher	developer

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

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires a Python environment with TensorFlow installed, some notebooks may need version-specific fixes noted in the README.

No license information was mentioned in the explanation.

So what is it?

"tensorflow-deep-learning" is the official code repository for the Zero to Mastery Deep Learning with TensorFlow course. It contains all the Jupyter notebooks, datasets, slides, and exercises used in the course, which teaches the foundations of deep learning and how to build neural networks for common problem types using TensorFlow and Keras. The course is structured around numbered notebooks that progressively cover more ground: TensorFlow fundamentals, regression, classification, computer vision with convolutional neural networks, transfer learning, natural language processing, and two milestone projects where you build a food image classifier and a paper-skimming tool. Each notebook includes both code and explanatory text, and comes with exercises and extra reading suggestions. The first 14 hours of video content, covering the first three notebooks, are available free on YouTube. The remaining modules, notebooks 03 through 10, are part of the paid Zero to Mastery Academy. An online book version of the course materials is also available for free. The repository has received multiple compatibility updates over the years as newer TensorFlow versions changed function names and APIs. If you are working through the course, the README includes a fixes section with guidance on adjustments needed for specific TensorFlow versions. The materials use Jupyter notebooks, which mix code cells and text cells, meaning you can read explanations and run the code in the same document. The course assumes some prior programming experience but does not require a background in mathematics or machine learning. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1

I'm on notebook 04 of the tensorflow-deep-learning course on convolutional neural networks. Show me how to build a CNN in TensorFlow/Keras to classify images into 10 categories, including the model architecture and training loop.

Prompt 2

Using the tensorflow-deep-learning course materials, show me how to apply transfer learning with a pretrained EfficientNet model to classify food images with minimal training data.

Prompt 3

I'm getting a TensorFlow API error running an older notebook from tensorflow-deep-learning. The function signature changed in TF 2.x, how do I update this code to work with the latest TensorFlow version?

Prompt 4

Help me complete the paper-skimming project from tensorflow-deep-learning: a model that reads a medical abstract and classifies each sentence as background, method, result, or conclusion.

Frequently asked questions