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1. numpy.ipynb
2. Linear Separators.ipynb
3. Non Linearity.ipynb
4. Code - Linear Separator.ipynb
5. Code - Non Linearity.ipynb
6. Universality - XOR.ipynb
7. Code - MNIST MLP.ipynb
README.md
TRUTH-TABLE-1.jpg
b_set.npy
circle_one_hot.npy
circles.npy
circles_xs.npy
circles_ys.npy
linear_primer.key
linear_primer.pdf
mnist.npz
r_b_one_hot.npy
r_set.npy
xor_xs.npy
xor_ys.npy

README.md

Lecture 2 - Linearity, Non-linearity, Simple Networks

This lecture starts with a few primers and builds up to a full neural network that can solve a non-trivial problem.

  • Linear Primer - We look at the nature of linearity and linear transformations
  • Numpy - We introduce the jupyter notebook and we look at numpy - the language spoken by tensorflow and pytorch (sort of).
  • Non-Linearity - We identify the limitations of linear transforms and exploit non-linearity with an illuminating example.
  • Code - Linear-Separator - We implement a small linear model in Keras.
  • Code - Non Linearity.ipynb - We express the non-linear thought experiment in code and look at the standard deep learning building blocks.
  • Code - Universality - We illustrate the universal nature of these building blocks by taking our existing code and training it on the XOR function.
  • Code - MNIST MLP.ipynb - We take building blocks covered over a few toy problems and solve a real computer vision problem.