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Introduction
Diving in Big Data
Big Data Vertical vs. horizontal scalability [slides ][drawing ] Scaling storage horizontally with K-V pairs [slides ][drawings ] Scaling processing horizontally with MapReduce [slides ][drawing ] Achieving usability implementing a SQL interfaces on horizontally scalable solutions [slides ]
Diving into AI
solving problems the AI way [drawings ] coding vs. machine learning/AI Gradient Descent as a general procedure Addressing dynamic pricing with linear regression (supervised machine learning) [drawings ] what is the relationship is not linear? Polinomial fitting vs. axis transforamtion (a circle is a line in x^{2} X y^{2} ) Detecting spam emails using Naive Bayes Algorithm [drawings ] Recommending Apps based on Decision Trees [drawings ][animation ] Deciding to accept students at a university based on Logistic Regression and Gradient Descent with Log-loss function [drawings ] When a line is not enough … or the kernel trick of Support Vector Machines [drawings ] Placing shops given distribution of buyers with desired segment solution using k-means (unsupervised machine learning) [drawings ] From a single neuron to deep learning visually using tensorflow playgroundhttps://playground.tensorflow.org classification of two groups that can be separated by a circle using multiple neurons (each tracing a line) complex classification
beware confirmation bias Ethics and legal implications