Deep Learning Track WiSe 24/25
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  • Welcome to the Deep Learning Track
  • Setup
  • Learning Material
  • Section 1 - The Mathchevron-right
  • Section 2 - The Datachevron-right
  • Section 3 - Neural Networkschevron-right
    • 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 Networkschevron-right
  • Final Project - Text Classification
  • Further Resourceschevron-right
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Section 3 - Neural Networks

We have finally worked through the basic math and has a glimpse introduction on how to load data with PyTorch. Now it is time to build our first neural network.

Activation Functionschevron-rightExercise - Activation Functionschevron-rightExercise - The Softmax Functionchevron-rightThe Neuronchevron-rightTwo type of applications: Regression and Classificationchevron-rightLoss Functionschevron-rightExercise - Regression Loss Functionschevron-rightExercise - Classification Loss Functionschevron-rightThe Gradient Descent Algorithmchevron-rightExercise - Implementing Gradient Descentchevron-rightExercise - PyTorch Autogradchevron-rightExercise - Regression with Neural Networkschevron-rightExercise - Classification with Neural Networkschevron-rightPlayground - Neural Networkschevron-right
PreviousWorking with Textchevron-leftNextActivation Functionschevron-right