An introduction to the early 19th-century philosophy of Transcendentalism and why it’s ideas on environmentalism and self-reliance are so integral today. Featured and translated for Greek newspaper Orthos Logos.

Exploring the AI of the 60’s and 70’s – rule-based systems. How microworlds and a SHRDLU, a robot that could seemingly understand human language, was actually just a fantastic rule-follower. Coding a simple SHRDLU in Python.

Using Logistic Regression for an image classification problem in PyTorch, and why it can’t get past 40% accuracy. Explanations on cross-entropy loss, softmax, one-hot encoding, and general logistic regression knowledge.

An introduction of the math behind artificial neural network backpropagation, and the use of the chain rule of calculus in the context of backpropagation.

How Gradient Descent works both theoretically and mathematically, with an explanation of gradients from multivariable calculus and why they matter in the context of linear regression. Also, a complete derivation of the update rules for paramaters.

Viewing survival as one big cost function, exploring local minima through dead-end evolution, and comparing and contrasting various parallels of natural selection and artificial intelligence.

An exploration into deep thinking – the benefits, and what it means to truly understand a subject. The war against memorization, taking notes, scheduling.

How to come up with a more efficient version of the Normal Equations to use with datasets that are continuously increasing. Seeing how this arises from the Sherman-Morrison-Woodbury Formula in Numerical Linear Algebra. Coding in Python.