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instillai/tensorflow-course

16,290Jupyter NotebookAudience · dataComplexity · 1/5Setup · easy

TLDR

A beginner-friendly collection of TensorFlow tutorials written as runnable Jupyter Notebooks, learn machine learning and neural networks step by step with clear, well-documented code examples.

Mindmap

mindmap
  root((repo))
    What it does
      TensorFlow tutorials
      Beginner friendly
      Runnable examples
    Format
      Jupyter Notebooks
      Interactive code
      Step by step
    Topics covered
      Neural networks
      Deep learning
      Data tasks
    Audience
      Beginners
      Intermediate devs
      Self-learners
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Code map

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Things people build with this

USE CASE 1

Learn TensorFlow from scratch by running and modifying beginner-friendly code examples in your browser.

USE CASE 2

Follow step-by-step notebooks to understand how neural networks are built and trained.

USE CASE 3

Use the structured tutorials as a hands-on reference when building your first machine learning project.

Tech stack

PythonTensorFlowJupyter Notebook

Getting it running

Difficulty · easy Time to first run · 30min

In plain English

TensorFlow-Course is a collection of beginner-friendly tutorials for learning TensorFlow, an open-source machine learning library originally developed by Google. The tutorials are designed to be simple, well-documented, and ready to run, addressing a common complaint about existing TensorFlow resources being either too complex or poorly explained. TensorFlow is a software framework used for building and training machine learning models, particularly neural networks (computational systems loosely inspired by the human brain, used for tasks like image recognition, language processing, and prediction). It uses dataflow programming, a model where computations are described as a graph of operations that data flows through. The course is structured as Jupyter Notebooks, interactive documents that combine text, code, and output in one place, letting you run and modify code step by step in your browser. Tutorials are organized into categories and cover topics from a basic "welcome" introduction up through more complex deep learning techniques. Each tutorial comes with source code and most include accompanying documentation. You would use this repository if you are a beginner or intermediate developer wanting to learn TensorFlow with clear, practical examples rather than dense documentation. The tutorials were updated to TensorFlow 2.3. It is written in Python and uses the Jupyter Notebook format, making it easy to follow along interactively. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1
Open the TensorFlow-Course beginner notebook and walk me through training my first neural network to classify images step by step.
Prompt 2
I'm learning TensorFlow 2.3, which notebook in this course should I start with and how do I run it locally?
Prompt 3
Modify the regression tutorial notebook from TensorFlow-Course to predict house prices using my own CSV dataset.
Prompt 4
How do I convert one of the TensorFlow-Course Jupyter Notebooks into a standalone Python script I can run from the terminal?
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