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BeginnerTensorFlow52 min

Handwritten Digits Classification

Classic handwritten digit classification tutorial using TensorFlow/Keras and CNN concepts.

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Handwritten Digits Classification

Overview

Classic handwritten digit classification tutorial using TensorFlow/Keras and CNN concepts. This reusable page template includes editable explanation sections, result screenshots, Python code blocks, a YouTube video link, and a download-code button.

Editing tip: This page is controlled from data/tutorials.ts. Change the title, text, images, Python code, YouTube link, and download link there.

What you will learn

  • Load handwritten digit dataset
  • Build a simple CNN
  • Train and evaluate model
  • Predict digit from a new image

Suggested tutorial flow

01

Concept and motivation

Explain the problem, inputs, outputs and why the tutorial is useful.

02

Environment and files

Mention required Python packages, dataset files, model weights and folder structure.

03

Implementation walkthrough

Explain code step by step with one or more Python code blocks.

04

Results and improvements

Add screenshots, output discussion, common errors and future improvements.

Text explanation template

Introduction

This tutorial explains Handwritten Digits Classification in a practical AI Code School style. It is designed as a written companion to your YouTube video and a place where students can find code, screenshots, notes and downloads.

Later you can replace this sample text with your full theory, project motivation, dataset description and step-by-step teaching explanation.

Required libraries and setup

Use this section for installation commands, package versions, folder structure and model/download requirements. Keep it beginner friendly so learners can reproduce the tutorial.

  • Python 3.10 or later
  • OpenCV, NumPy and supporting packages
  • PyTorch or TensorFlow if the tutorial uses deep learning
  • Input images, dataset, trained model or notebook files

Step-by-step implementation notes

Explain your code in the same order as the video. Start with imports and setup, then preprocessing, model loading, inference, result visualization and troubleshooting.

  • Load handwritten digit dataset
  • Build a simple CNN
  • Train and evaluate model
  • Predict digit from a new image

Results and discussion

Replace the placeholder result images with your own screenshots, model outputs, graphs, confusion matrices or webcam/video snapshots. Add a short explanation under each image so the page becomes useful even without watching the video.

How to modify this tutorial later

To update this page, open data/tutorials.ts and change the textSections, resultImages, codeBlocks, videoUrl and download fields. To add new screenshots, place images under public/images/tutorials/results/ and reference them in the resultImages array.

Images, screenshots and results

Add your result screenshots, graphs, model outputs, detected objects, confusion matrices, or interface snapshots here.

Handwritten Digits Classification result image 1
Input/preprocessing placeholder — replace with your actual input or data preparation screenshot.
Handwritten Digits Classification result image 2
Model output placeholder — replace with your detection/classification/OCR/pose result screenshot.
Handwritten Digits Classification result image 3
Evaluation placeholder — replace with your training curve, confusion matrix, accuracy graph or final output.

Python code explanation

Use one or more code blocks. After each block, explain what the code does in simple teaching language.

Train a simple CNN on digits

pythonexample
from tensorflow.keras.datasets import mnist
from tensorflow.keras import layers, models

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(-1, 28, 28, 1) / 255.0
x_test = x_test.reshape(-1, 28, 28, 1) / 255.0

model = models.Sequential([
    layers.Conv2D(32, 3, activation="relu", input_shape=(28, 28, 1)),
    layers.MaxPooling2D(),
    layers.Flatten(),
    layers.Dense(64, activation="relu"),
    layers.Dense(10, activation="softmax")
])
model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
model.fit(x_train, y_train, epochs=3, validation_data=(x_test, y_test))

This starter CNN can be replaced by your full notebook/code from the actual tutorial.

Download source code

Use this area to provide the complete source code, GitHub repository, Google Drive ZIP, notebook, dataset, or premium download link.

Download Sample Source Code
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