PyTorch CNN for MNIST: From Data Loading to 99% Accuracy скачать в хорошем качестве

PyTorch CNN for MNIST: From Data Loading to 99% Accuracy

Build a Convolutional Neural Network (CNN) for MNIST using PyTorch from scratch in this complete tutorial. We start by loading the original MNIST IDX files, perform exploratory data analysis (EDA), visualize handwritten digits, and then build and train a CNN baseline that achieves ~99% accuracy. This tutorial is ideal for students, beginners, and ML practitioners who want a clean, end-to-end PyTorch workflow for image classification. What you’ll learn: 1. Loading MNIST IDX dataset in Google Colab 2. Exploratory Data Analysis (EDA) for image data 3. Visualizing handwritten digit samples 4. Preprocessing & normalization for CNNs 5. Building a CNN in PyTorch 6. Training & evaluating the model 7. Understanding CNN performance on MNIST Tools & Libraries: 1. Python 2. PyTorch 3. NumPy 4. Matplotlib 🔗 Colab Notebook (Follow Along): https://colab.research.google.com/dri... Timestamps: 0:00 Introduction 1:15 Loading MNIST Dataset 3:30 Exploratory Data Analysis (EDA) 6:45 Visualizing Sample Images 9:20 Data Preprocessing 11:00 CNN Architecture (PyTorch) 14:30 Model Training 17:50 Evaluation & Accuracy 20:15 Key Takeaways