Deep Learning track

Our curriculum is now organized not into tracks, but into modules described here . But for your information, we include here a description of the Deep Learning track before the reorganization.



Regression problems

Classification problems

Performance evaluation, result significance, and common mistakes

  • Performance metrics and their relationship to the problem’s practical objective
  • Dealing with imbalanced datasets
  • Choosing the right baselines
  • Ablation studies
  • Significance of results

  • Supervised machine learning: basics

    Optimization methods

    Deep Learning libraries

  • Computation graphs
  • Graph-based computations vs. eager execution
  • TensorFlow and Keras
  • PyTorch
  • Production considerations
  • Research considerations

  • Practical aspects of training neural networks

    Practical aspects of training machine learning models

    Improving the performance of machine-learning models

    Neural networks based on fully connected layers

    Convolutional neural networks (CNN)

    Recurrent neural networks (RNN)

    Neural networks with entity embedding layers

    Natural Language Processing (NLP)

    Unsupervised machine learning: basics


    Generative Adversarial Networks (GAN)

    Vulnerabilities of machine-learning systems

    Time-series analysis

    Causal inference

    Reinforcement learning


    Computational aspects

    Command line interfaces and operating systems

    GPU computing

    Optional: Building one's own GPU-powered computer

    Python: language structure

    Python: libraries

    Python: computation speed

    Data formats


    Cloud computing