Deep Learning Track WiSe 24/25
Deep Learning Track WiSe 24/25
Deep Learning Track WiSe 24/25
  • Welcome to the Deep Learning Track
  • Setup
  • Learning Material
  • Section 1 - The Math
    • Derivatives and Gradients
    • Vectors, Matrices and Tensors
    • The power of matrix computation
    • Exercise - Matrix Computation
  • Section 2 - The Data
    • PyTorch Datasets and Data Loaders
    • Working with Data Tables
    • Exercise - Loading Data from a CSV file
    • Working with Images
    • Exercise - Image Datasets
    • Working with Text
  • Section 3 - Neural Networks
    • Activation Functions
    • Exercise - Activation Functions
    • Exercise - The Softmax Function
    • The Neuron
    • Two type of applications: Regression and Classification
    • Loss Functions
    • Exercise - Regression Loss Functions
    • Exercise - Classification Loss Functions
    • The Gradient Descent Algorithm
    • Exercise - Implementing Gradient Descent
    • Exercise - PyTorch Autograd
    • Exercise - Regression with Neural Networks
    • Exercise - Classification with Neural Networks
    • Playground - Neural Networks
  • Section 4 - Convolutional Neural Networks
    • Convolution
    • Convolutional Neural Networks
    • Classifying handwritten digits
    • Playground - Convolutional Neural Networks
    • Transfer Learning
  • Final Project - Text Classification
  • Further Resources
    • Computer Vision Libraries
    • Image Classification with PyTorch
    • Object Detection with PyTorch
    • Deep AI Explainability
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  1. Section 2 - The Data

Exercise - Image Datasets

We will continue to build datasets, but this time we will load images.

PreviousWorking with ImagesNextWorking with Text

Implement the missing parts in this notebook. You can work with these sample images:

15KB
Image Datasets.ipynb