MLG 001 Introduction
Feb 01, 2017 (updated Oct 20, 2021)
Click to Play Episode
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.
Resources
Resources best viewed here
Loading...
Show Notes
Sitting for hours drains energy and focus. A walking desk boosts alertness, helping you retain complex ML topics more effectively.Boost focus and energy to learn faster and retain more.Discover the benefitsDiscover the benefits
- 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