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Google

Google DeepMind: 03 Design And Train Neural Networks

Google via Google Skills

Overview

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In this Google DeepMind course you will focus on the training process for machine learning models. You will learn how to spot and mitigate issues when training a model, such as overfitting and underfitting. In practical coding labs, you will implement and evaluate the multilayer perceptron for simple classification tasks. This will provide insights into the mechanics of training a neural network model and the backpropagation algorithm. Research case studies will demonstrate how neural networks power real-world models. Additionally, you will consider the broader social impacts of innovation by looking beyond immediate benefits to anticipate potential risks, safety concerns, and further-reaching societal consequences.

Syllabus

  • Signal and noise
    • The deep learning revolution
    • Signal and noise
    • Lab: Distinguish Between Signal and Noise
    • Learning objectives
    • How to get the most out of this course
  • Generalization
    • Generalization and the bias-variance trade-off
    • Training and test splits
    • Predicting cyclones with AI weather models
    • Anticipating risks
    • Knowledge check 1
  • The multilayer perceptron
    • The multilayer perceptron (MLP)
    • Lab: Make Predictions with a Single-Layer Neural Network
    • Lab: Separate More Complex Data
    • Modeling data that is not linearly-separable
    • Lab: Design Your Own MLP
    • Knowledge check 2
  • Preventing overfitting and improving generalization
    • Lab: Tune Hyperparameters
    • Methods to mitigate overfitting
    • Lab: Mitigate Overfitting
    • Why is a validation dataset needed?
    • Alignment and safety
    • Knowledge check 3
  • What are gradients?
    • Gradients
    • Lab: Gradients
    • Knowledge check 4
  • Backpropagation
    • Backpropagation
    • Stochastic gradient descent (SGD)
    • Lab: Train Your Model with Keras
    • Knowledge check 5
  • Challenge
    • Anticipating social impacts
    • Create an impact statement card
    • Knowledge check 6
  • Continue your journey
    • Summary
    • Additional resources and further reading
    • Looking forward
    • Glossary
    • Feedback
  • Your Next Steps
    • Claim credential

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