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Podcast: Machine Learning Guide
Episode:

1. Introduction
Category: Technology
Duration: 00:12:35
Publish Date: 2017-01-31 23:00:00
Description:

Introduction to the Machine Learning Guide

Who am I: Tyler Renelle (https://www.linkedin.com/in/lefnire)

What is this podcast? - "Middle" level overview (deeper than a bird's eye view of machine learning; higher than math equations) - No math/programming experience required

Who is it for - Anyone curious about machine learning fundamentals - Aspiring machine learning developers (maybe transitioning from web/mobile development)

Why audio? - Supplementary content for commute/exercise/chores will help solidify your book/course-work

What it's not - News and Interviews ** TWiML and AI (https://twimlai.com) ** O'Reilly Data Show (https://www.oreilly.com/topics/oreilly-data-show-podcast) ** Talking machines (http://www.thetalkingmachines.com/) - Misc Topics ** Linear Digressions (http://lineardigressions.com/) ** Data Skeptic (https://dataskeptic.com/) ** Partially Derivative (http://partiallyderivative.com/) - iTunesU issues - Learning machines 101 (http://www.learningmachines101.com/)

Planned episodes - What is AI/ML: definition, comparison, history - Inspiration: automation, singularity, consciousness - ML Intuition: learning basics (infer/error/train); supervised/unsupervised/reinforcement; applications - Math overview: linear algebra, statistics, calculus - Linear models: supervised (regression, classification); unsupervised - Parts: regularization, performance evaluation, dimensionality reduction, etc - Deep models: neural networks, recurrent neural networks (RNNs), convolutional neural networks (convnets/CNNs) - Languages and Frameworks: Python vs R vs Java vs C/C++ vs MATLAB, etc; TensorFlow vs Torch vs Theano vs Spark, etc
Total Play: 2

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