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
Powered by GitBook
On this page
  1. Section 3 - Neural Networks

Exercise - Implementing Gradient Descent

In this notebook you will implement the Gradient Descent Algorithm and optimize a linear function so that it fits a set of data points.

PreviousThe Gradient Descent AlgorithmNextExercise - PyTorch Autograd

In this notebook you will optimize a linear function of the form $a*x$ that has only one parameter. The goal is that your model learns based on data which value the parameter should have to match the true function.

29KB
Gradient Descent.ipynb
The green function is the true function, the orange one is the model. Your task will be to optimize the model with the gradient descent algorithm so that it comes closer to the true function.