• MLG 001 Introduction

  • Feb 1 2017
  • Length: 9 mins
  • Podcast

  • Summary

  • Support my new podcast: Lefnire's Life Hacks

    Show notes: ocdevel.com/mlg/1. MLG teaches the fundamentals of machine learning and artificial intelligence. It covers intuition, models, math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc.

    • MLG, Resources Guide
    • Gnothi (podcast project): website, Github
    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

    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, O'Reilly Data Show, Talking machines
    • Misc Topics: Linear Digressions, Data Skeptic, Learning machines 101
    • iTunesU issues

    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
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