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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
- "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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