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Deep Learning

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Shivon Zilis - Machine Intelligence Machine Intelligence in the Real World (this pieces was originally posted on Tech Crunch) . I’ve been laser-focused on machine intelligence in the past few years. David MacKay: Information Theory, Inference, and Learning Algorithms: The Book Download the book too You can browse and search the book on Google books. You may download The book in one file (640 pages):

Machine Learning in 7 Pictures Basic machine learning concepts of Bias vs Variance Tradeoff, Avoiding overfitting, Bayesian inference and Occam razor, Feature combination, Non-linear basis functions, and more - explained via pictures. By Deniz Yuret, Feb 2014. I find myself coming back to the same few pictures when explaining basic machine learning concepts. A Neural Network Playground Um, What Is a Neural Network? It’s a technique for building a computer program that learns from data. It is based very loosely on how we think the human brain works. First, a collection of software “neurons” are created and connected together, allowing them to send messages to each other.

Intelligence matters: Artificial intelligence and algorithms Welcome back. Please sign in. Welcome back. {* #userInformationForm *} {* traditionalSignIn_emailAddress *} {* traditionalSignIn_password *} Jupyter Notebook Viewer Probabilistic Programming & Bayesian Methods for Hackers¶ Using Python and PyMC¶ The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples.

Occam’s Razor and PAC-learning So far our discussion of learning theory has been seeing the definition of PAC-learning, tinkering with it, and seeing simple examples of learnable concept classes. We’ve said that our real interest is in proving big theorems about what big classes of problems can and can’t be learned. One major tool for doing this with PAC is the concept of VC-dimension, but to set the stage we’re going to prove a simpler theorem that gives a nice picture of PAC-learning when your hypothesis class is small. In short, the theorem we’ll prove says that if you have a finite set of hypotheses to work with, and you can always find a hypothesis that’s consistent with the data you’ve seen, then you can learn efficiently. It’s obvious, but we want to quantify exactly how much data you need to ensure low error. This will also give us some concrete mathematical justification for philosophical claims about simplicity, and the theorems won’t change much when we generalize to VC-dimension in a future post.

Where Computers Defeat Humans, and Where They Can’t Photo ALPHAGO, the artificial intelligence system built by the Google subsidiary DeepMind, has just defeated the human champion, Lee Se-dol, four games to one in the tournament of the strategy game of Go. Why does this matter? After all, computers surpassed humans in chess in 1997, when IBM’s Deep Blue beat Garry Kasparov. Introduction to Statistical Learning An Introduction to Statistical Learning with Applications in R Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

Probably Approximately Correct — a Formal Theory of Learning In tackling machine learning (and computer science in general) we face some deep philosophical questions. Questions like, “What does it mean to learn?” and, “Can a computer learn?” and, “How do you define simplicity?” and, “Why does Occam’s Razor work? Deep Learning Can I get a PDF of this book? No, our contract with MIT Press forbids distribution of too easily copied electronic formats of the book. Google employees who would like a paper copy of the book can send Ian the name of the printer nearest their desk and he will send a print job to that printer containing as much of the book as you would like to read.Why are you using HTML format for the drafts? This format is a sort of weak DRM. It's intended to discourage unauthorized copying/editing of the book. Unfortunately, the conversion from PDF to HTML is not perfect, and some things like subscript expressions do not render correctly.

Tool: 31 Resources to Learn AI & Deep Learning, From Beginner to Advanced — Humanizing Technology Tool: 31 Resources to Learn AI & Deep Learning, From Beginner to Advanced I’ve spent the last few weeks learning, well, about deep learning. I’ve parsed through the internet, read a ton, and tried to get a sense of where we are as a community. 13 Free Self-Study Books on Mathematics, Machine Learning & Deep Learning Introduction Getting learners to read textbooks and use other teaching aids effectively can be tricky. Especially, when the books are just too dreary. In this post, we’ve compiled great e-resources for you digital natives looking to explore the exciting world of Machine Learning and Neural Networks.

Theoretical Motivations for Deep Learning This post is based on the lecture “Deep Learning: Theoretical Motivations” given by Dr. Yoshua Bengio at Deep Learning Summer School, Montreal 2015. I highly recommend the lecture for a deeper understanding of the topic.

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