Getting started

• Overview: the machine learning process

• Pytorch & other tools, installation

• Project: Fashion MNIST

Tensors

• What's a tensor?

• Operations on tensors

• Tensor calculus

• Project: Images

Differentiation

• Differential calculus

  • Derivative, partial derivative

  • Gradient, Jacobian Matrix,

  • Differential, Chain rule

  • higher order differential calculus

• Differentiable programming:

  • Automatic differentiation

  • Computation graph

  • Forward/backward differentiation

  • In Pytorch

  • Tensor, Variable, Function

• Project: Hamiltonian mechanics (?)

Optimization

• Concepts

• Torch optimizers

• Project: Quadratic function / Rosenbrock function / Map (?)

Datasets

• Tables & pandas

• ... (structure & formats & tools "in the wild"?)

Machine learning

• Linear models

• Logistic regression

• Neural networks

📖 🇫🇷 Calcul Différentiel, Intégrale et Stochastique by Sébastien Boisgérault, Thomas Romary, Emilie Chautru et Pauline Bernard.

📖 🇺🇸 Elements of Differentiable Programming by Mathieu Blondel and Vincent Roulet.

📖 🇺🇸 The Little Book of Deep Learning by François Fleuret.

📖 🇺🇸 Learning Theory from First Principles by Francis Bach.

📖 🇺🇸 Scientific Visualization: Python + Matplotlib by Nicolas Rougier.