Articles#

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• Deep reinforcement learning with Pong from pixels
  • Prerequisites
  • Table of contents
    • A note on RL and deep RL
    • Deep RL glossary
  • Set up Pong
  • Preprocess frames (the observation)
  • Create the policy (the neural network) and the forward pass
  • Set up the update step (backpropagation)
  • Define the discounted rewards (expected return) function
  • Train the agent for a number of episodes
  • Next steps
  • Appendix
    • Notes on RL and deep RL
    • How to set up video playback in your Jupyter notebook
• Sentiment Analysis on notable speeches of the last decade
  • Prerequisites
  • Table of contents
  • 1. Data Collection
    • Collecting the IMDb reviews dataset
    • Collecting and loading the speech transcripts
  • 2. Preprocess the datasets
  • 3. Build the Deep Learning Model
    • Introduction to a Long Short Term Memory Network
    • Overview of the Model Architecture
    • Forward Propagation
    • But how do you obtain sentiment from the LSTM’s output?
    • Backpropagation
    • Updating the Parameters
    • Training the Network
    • Sentiment Analysis on the Speech Data
  • Looking at our Neural Network from an ethical perspective
  • Next Steps