#python

Learn AI Series (#149) - AI Ethics in Practice What will I learn Bias auditing -- how to systematically detect and MEASURE bias in a model before it ever reaches a user, with a reusable auditor you can

Learn AI Series (#148) - The Economics of AI What will I learn AI and labor markets -- why AI eats TASKS and not whole jobs, why this wave hits white-collar cognitive work in a way no previous wave did,

Learn AI Series (#147) - AI Safety and Alignment What will I learn The alignment problem -- why getting an AI to do what you actually MEANT is a much nastier problem than getting it to do what you literally

Learn AI Series (#146) - Explainability and Interpretability What will I learn The black box problem and why it is not an academic worry -- trust, debugging, and (increasingly) the law all demand that

Learn AI Series (#145) - Neuro-Symbolic AI What will I learn Why the two oldest tribes in AI keep failing at each other's job: neural nets that perceive brilliantly but reason fuzzily, symbolic systems

Learn AI Series (#144) - Few-Shot and Zero-Shot Learning What will I learn The N-way K-shot setup: the vocabulary of few-shot learning, and why we train in "episodes" that simulate learning from

Learn AI Series (#143) - Continual Learning What will I learn Catastrophic forgetting: why a neural network trained on a new task quietly destroys what it knew about the old one, and the parameter-sharing

Después de ver algunas clases de Phyton en la universidad de Stanford en su programa Code in Place, debíamos hacer un proyecto, yo estuve ideando tantas pero tantas cosas que tuve burnout, sobre todo por

Learn AI Series (#142) - AI Reasoning and Planning What will I learn The System 1 vs System 2 framing (fast intuition vs slow deliberation) and why a plain transformer is a System 1 machine bolted shut

Learn AI Series (#141) - Robotics and Embodied AI What will I learn The sim-to-real gap: why a policy that is flawless in simulation faceplants in reality, and how domain randomization quietly turns that

Learn AI Series (#140) - Scientific AI What will I learn How AlphaFold cracked the 50-year protein folding problem, and which pieces of THIS series it quietly reused to do it; AI for drug discovery --

Learn AI Series (#139) - AI for Code What will I learn How code generation models actually work under the hood -- the training data, the objective, and what "writing code" really means to a language

Learn AI Series (#138) - Multimodal AI What will I learn What multimodal AI actually means, and why gluing modalities together is genuinely more than the sum of the parts; the three fusion architectures

Learn AI Series (#137) - Foundation Models What will I learn What actually makes a model "foundational" -- and why that word is doing a lot more work than it looks; the common pre-training recipe

Learn AI Series (#136) - Mini Project: Production AI Platform What will I learn How to wire the whole production arc from episodes #117-135 into ONE running system, not nineteen loose parts; a file-based

Learn AI Series (#135) - Building AI Teams and Processes What will I learn The distinct roles in an AI team and why they do not map cleanly onto the frontend/backend/QA boxes you already know; the ML lifecycle

Learn AI Series (#134) - AI Infrastructure Economics What will I learn What GPU compute actually costs across the big cloud providers -- and why the same A100 can cost you three times more depending on

Learn AI Series (#133) - Synthetic Data Generation What will I learn You will learn why synthetic data even exists -- the three itches it scratches: class imbalance, data scarcity, and privacy; CTGAN,

Learn AI Series (#132) - AI for Structured Data What will I learn You will learn why deep learning keeps losing to gradient boosting on tabular data -- the single most common data format in the entire

While building my chess project in Python (in Thonny), my goal was to create more than just a “board with pieces”. I wanted a practical desktop application that lets you play against a chess engine or