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Course Progression

If you would like a smooth transition in learning deep learning concepts, you need to follow the materials in a sequential order. Some sections are still pending as I am working on them.

1. Practical Deep Learning with PyTorch

  • Matrices
  • Gradients
  • Linear Regression
  • Logistic Regression
  • Feedforward Neural Networks (FNN)
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Long Short Term Memory Neural Networks (LSTM)
  • Autoencoders (AE)
  • Variational Autoencoders (VAEs)
  • Adversarial Autoencoders (AAEs)
  • Generative Adversarial Networks (GANs)

2. Boosting Deep Learning Models with PyTorch

  • Derivatives, Gradients and Jacobian
  • Gradient Descent and Backpropagation
  • Learning Rate Scheduling
  • Optimizers
  • Advanced Learning Rate Optimization
  • Weight Initializations and Activation Functions
  • Overfitting Prevention
  • Loss, Accuracy and Weight Visualizations

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