Foundations of ML and AI: Book recommendations
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Foundations of ML and AI: Book recommendations
1–10 of 18 posts
Re: Foundations of ML and AI: Book recommendations
#2Original title: Programmer, Teach Yourself Foundations of ML and AI with these 6 Books
Books:
- Ian Hacking - Introduction to probability and Inductive Logic
- John Kruschke - Doing Bayesian Data Analysis
- Gareth Williams - Linear Algebra with Applications, Alternate Edition
- Matthew Scarpino - OpenCL in Action
- Michael Nielsen - Neural Networks and Deep Learning
- Goodfellow, Bengio, and Courville - Deep Learning
Re: Foundations of ML and AI: Book recommendations
#3Looks cool!
Re: Foundations of ML and AI: Book recommendations
#4Original title: Programmer, Teach Yourself Foundations of ML and AI with these 6 Books Books: - Ian Hacking - Introduction to probability and Inductive Logic - John Kruschke - Doing Bayesian Data Analysis - Gareth Williams - Linear Algebra with Applications, Alternate Edition - Matthew Scarpino - OpenCL in Action - Michael Nielsen - Neural Networks and Deep Learning - Goodfellow, Bengio, and Courville - Deep Learning
Nice List!
Re: Foundations of ML and AI: Book recommendations
#5Original title: Programmer, Teach Yourself Foundations of ML and AI with these 6 Books Books: - Ian Hacking - Introduction to probability and Inductive Logic - John Kruschke - Doing Bayesian Data Analysis - Gareth Williams - Linear Algebra with Applications, Alternate Edition - Matthew Scarpino - OpenCL in Action - Michael Nielsen - Neural Networks and Deep Learning - Goodfellow, Bengio, and Courville - Deep Learning
How to teach yourself ML in two easy steps
1) Have a strong knowledge of undergraduate mathematics, probability, statistics, numerics
2) read a book about machine learning
Re: Foundations of ML and AI: Book recommendations
#6Original title: Programmer, Teach Yourself Foundations of ML and AI with these 6 Books Books: - Ian Hacking - Introduction to probability and Inductive Logic - John Kruschke - Doing Bayesian Data Analysis - Gareth Williams - Linear Algebra with Applications, Alternate Edition - Matthew Scarpino - OpenCL in Action - Michael Nielsen - Neural Networks and Deep Learning - Goodfellow, Bengio, and Courville - Deep Learning
How to teach yourself ML in two easy steps 1) Have a strong knowledge of undergraduate mathematics, probability, statistics, numerics 2) read a book about machine learning
Piggybacking on this, I instead recommend an introductory ML book like Bishop or Murphy, a statistical ML book like Mohri or Shai Shalev-Schwartz, and a textbook on nonlinear optimization, convex or otherwise.
The jump from classical machine learning to deep learning is not far if you have a good understanding of first principles.
Re: Foundations of ML and AI: Book recommendations
#7Original title: Programmer, Teach Yourself Foundations of ML and AI with these 6 Books Books: - Ian Hacking - Introduction to probability and Inductive Logic - John Kruschke - Doing Bayesian Data Analysis - Gareth Williams - Linear Algebra with Applications, Alternate Edition - Matthew Scarpino - OpenCL in Action - Michael Nielsen - Neural Networks and Deep Learning - Goodfellow, Bengio, and Courville - Deep Learning
What's the legality of making your own pdf of Goodfellow's book from the html?
Re: Foundations of ML and AI: Book recommendations
#8Pattern Recogntion and Machine Learning by Bishop
I’m highly skeptical of lists that do not include this standard text.
Re: Foundations of ML and AI: Book recommendations
#9Original title: Programmer, Teach Yourself Foundations of ML and AI with these 6 Books Books: - Ian Hacking - Introduction to probability and Inductive Logic - John Kruschke - Doing Bayesian Data Analysis - Gareth Williams - Linear Algebra with Applications, Alternate Edition - Matthew Scarpino - OpenCL in Action - Michael Nielsen - Neural Networks and Deep Learning - Goodfellow, Bengio, and Courville - Deep Learning
What's the legality of making your own pdf of Goodfellow's book from the html?
I’m not sure; I personally saved copies for myself, which I assume is okay. I believe (but could be mistaken) that it’s more about redistribution than consumption.
Re: Foundations of ML and AI: Book recommendations
#10Pattern Recogntion and Machine Learning by Bishop I’m highly skeptical of lists that do not include this standard text.
PRML was my introductory textbook, and it’s excellent. (The neural network chapters are a quite good introduction.) I also hear good things about Kevin Murphy’s text, which brings a more Bayesian approach and is preferred by some instructors I know, but I’ve never used it.