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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, and they will have the ⏳ icon beside 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
  • ⏳ Data Preprocessing for Images and Videos
  • ⏳ Data Preprocessing for Time Series

3. Deep Model-Free Reinforcement Learning with PyTorch

  • Supervised Learning to Reinforcement Learning
  • ⏳ Markov Decision Processes and Bellman Equations
  • ⏳ Dynamic Programming
  • ⏳ Monte Carlo Approach
  • ⏳ Temporal-Difference
  • ⏳ Policy Gradient: REINFORCE
  • ⏳ Policy Gradient: Actor-Critic
  • ⏳ Policy Gradient: A2C/A3C
  • ⏳ Policy Gradient: ACKTR
  • ⏳ Policy Gradient: PPO
  • ⏳ Policy Gradient: DPG
  • ⏳ Policy Gradient: DDPG (DQN & DPG)
  • TBC...

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