Show notes: <a href="https://ocdevel.com/mlg/1" rel="noopener" target="_blank">ocdevel.com/mlg/1</a>. 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.
<ul>
<li><a href="https://ocdevel.com/mlg" rel="noopener" target="_blank">MLG</a>, <a href="https://ocdevel.com/mlg/resources" rel="noopener" target="_blank">Resources Guide</a></li>
<li>Gnothi (podcast project): <a href="https://gnothiai.com/" rel="noopener" target="_blank">website</a>, <a href="https://github.com/lefnire/gnothi" rel="noopener" target="_blank">Github</a></li>
<li><a href="https://ocdevel.com/blog/20240111-tylers-setup">Tyler's Battlestation</a></li>
</ul>
 What is this podcast? 
<ul>
<li>“Middle” level overview (deeper than a bird's eye view of machine learning; higher than math equations)</li>
<li>No math/programming experience required</li>
</ul>
Who is it for
<ul>
<li>Anyone curious about machine learning fundamentals</li>
<li>Aspiring machine learning developers</li>
</ul>
Why audio?
<ul>
<li>Supplementary content for commute/exercise/chores will help solidify your book/course-work</li>
</ul>
What it's not
<ul>
<li>News and Interviews: TWiML and AI, O'Reilly Data Show, Talking machines</li>
<li>Misc Topics: Linear Digressions, Data Skeptic, Learning machines 101</li>
<li>iTunesU issues</li>
</ul>
Planned episodes
<ul>
<li>What is AI/ML: definition, comparison, history</li>
<li>Inspiration: automation, singularity, consciousness</li>
<li>ML Intuition: learning basics (infer/error/train); supervised/unsupervised/reinforcement; applications</li>
<li>Math overview: linear algebra, statistics, calculus</li>
<li>Linear models: supervised (regression, classification); unsupervised</li>
<li>Parts: regularization, performance evaluation, dimensionality reduction, etc</li>
<li>Deep models: neural networks, recurrent neural networks (RNNs), convolutional neural networks (convnets/CNNs)</li>
<li>Languages and Frameworks: Python vs R vs Java vs C/C++ vs MATLAB, etc; TensorFlow vs Torch vs Theano vs Spark, etc</li>
</ul>

Tyler Renelle

MLG 001 Introduction

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, [&hellip;]

Machine Learning Guide Episode #1

MLG 001 Introduction