Reference

Glossary

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• artificial intelligence
• machine learning
• deep learning
• neural network
• convolutional neural network (CNN)
• recurrent neural network (RNN)
• accuracy
• epoch
• learning rate
• confusion matrix
• class imbalance
• overfitting
• hidden layer

External references

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Here is a (non exhaustive) list of external resources for further study after this lesson:

Miscellaneous resources

• the difference between validation data and test data
• underfitting and overfitting
• Unbalanced data
• Unbalanced data in Keras
• Tensorflow Playground, for visualizing neural networks
• ChatGPT prompt engineering course

Some ML challenges or benchmarks

• https://mlcontests.com/
• Kaggle, machine learning competitions
• protein structure prediction
• prediction of protein-protein interactions

Some courses for deeper learning:

• Fast AI course: making neural nets uncool again
• Intro to Deep Learning with PyTorch, the course is quite intuitive
• Coursera courses by Andrew Ng:
  • AI for everyone, for beginners who won’t do ML projects but are courious about what AI really is and what AI can do
  • ML course and DL course, quite intensive courses for beginner/intermediate-level researchers who will do ML/DL projects
  • Structuring Machine Learning Projects, how to conduct ML projects with useful ML engineering strategies
  • Book on Machine Learning
• Book: Ian Goodfellow and Yoshua Bengio and Aaron Courville - Deep Learning. A really thorough, detailed (though math-heavy) book on everything (for example Generative Adverserial Networks or Autoencoders) you want to know about deep learning
• Book: Simon J.D. Prince - Understanding Deep Learning. A less dense and slightly more modern overview of deep learning with coding examples for each chapter.