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Building and Training a Neural Network in Practice

Artificial Intelligence · Chapter 5 · Unit 3: Practical Implications of ANN

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Topics 6

  1. Data preparation with NumPy and Pandas
  2. Preprocessing with Scikit-learn: scaling, encoding categorical variables, train-test split
  3. Building the network with Keras on TensorFlow
  4. Training with fit(): epochs and batch size
  5. Model evaluation: accuracy, precision and recall; visualising metrics with Matplotlib
  6. Hyperparameter tuning: grid search and random search over learning rate, batch size, hidden layers and neurons

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