TensorFlow Probability: Learning with confidence
TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, and ML researchers/practitioners who want to encode domain knowledge to understand data and make predictions with uncertainty estimates. In this talk we focus on the "layers" module and demonstrate how TFP "distributions" fit naturally with Keras to enable estimating aleatoric and/or epistemic uncertainty.
Website: https://www.tensorflow.org/probability
Introduction Video: https://www.youtube.com/watch?v=BrwKURU-wpk
#tensorflow #machine_learning
TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, and ML researchers/practitioners who want to encode domain knowledge to understand data and make predictions with uncertainty estimates. In this talk we focus on the "layers" module and demonstrate how TFP "distributions" fit naturally with Keras to enable estimating aleatoric and/or epistemic uncertainty.
Website: https://www.tensorflow.org/probability
Introduction Video: https://www.youtube.com/watch?v=BrwKURU-wpk
#tensorflow #machine_learning
TensorFlow
TensorFlow Probability
A library to combine probabilistic models and deep learning on modern hardware (TPU, GPU) for data scientists, statisticians, ML researchers, and practitioners.
Mathematics for Machine Learning
Summary: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
https://mml-book.github.io/book/mml-book.pdf
#machine_learning #mathematics
Summary: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
https://mml-book.github.io/book/mml-book.pdf
#machine_learning #mathematics
Yann LeCuN advice for an undergraduate student who aspires to become a Machine Learning Scientist in the field of Deep Learning
(0) take all the continuous math and physics class you can possibly take. If you have the choice between “iOS programming” and “quantum mechanics”, take “quantum mechanics”. In any case, take Calc I, Calc II, Calc III, Linear Algebra, Probability and Statistics, and as many physics courses as you can. But make sure you learn to program.
(1) Take an AI-related problem you are passionate about.
(2) think about it on your own
(3) once you have formed your own idea of it, start reading the literature on the problem
(4) you will find that (a) your ideas were probably a bit naive but (b) your view of the problem is slightly different from what was done before.
(5) Find a professor in your school that can help you make your ideas concrete. It might be difficult. Professors are busy and don’t have much time for undergrads. The ones with the most free time are the very junior, the very senior, and the ones who are not very active in research.
(6) If you don’ find a professor with spare time, hook up with a postdoc or PhD student in his/her lab.
(7) ask the professor if you can attend his/her lab meetings and seminars or sit in his/her class.
(8) Before you graduate, try to write a paper about your research or release a piece of open source code.
(9) Now apply to PhD programs. Forget about the “ranking” of the school for now. Find a reputable professor who works on topics that you are interested in. Pick a person whose papers you like or admire.
(10) Apply to several PhD programs in the schools of the above-mentioned professors and mention in your letter that you’d like to work with that professor but would be open to work with others.
(11) ask your undergrad professor to write a recommendation letter for you. It’s maximally efficient if your undergrad professor is known by your favorite PhD advisor.
(12) if you don’t get accepted in one of your favorite PhD programs, get a job at Facebook or Google and try to get a gig as an engineer assisting research scientists at FAIR or Google Brain.
(13) publish a papers with the research scientists in question. Then re-apply to PhD programs and ask the FAIR or Google scientists you work with to write a recommendation letter for you.
https://www.quora.com/What%E2%80%99s-your-advice-for-undergraduate-student-who-aspires-to-be-a-research-scientist-in-deep-learning-or-related-field-one-day
#machine_learning
(0) take all the continuous math and physics class you can possibly take. If you have the choice between “iOS programming” and “quantum mechanics”, take “quantum mechanics”. In any case, take Calc I, Calc II, Calc III, Linear Algebra, Probability and Statistics, and as many physics courses as you can. But make sure you learn to program.
(1) Take an AI-related problem you are passionate about.
(2) think about it on your own
(3) once you have formed your own idea of it, start reading the literature on the problem
(4) you will find that (a) your ideas were probably a bit naive but (b) your view of the problem is slightly different from what was done before.
(5) Find a professor in your school that can help you make your ideas concrete. It might be difficult. Professors are busy and don’t have much time for undergrads. The ones with the most free time are the very junior, the very senior, and the ones who are not very active in research.
(6) If you don’ find a professor with spare time, hook up with a postdoc or PhD student in his/her lab.
(7) ask the professor if you can attend his/her lab meetings and seminars or sit in his/her class.
(8) Before you graduate, try to write a paper about your research or release a piece of open source code.
(9) Now apply to PhD programs. Forget about the “ranking” of the school for now. Find a reputable professor who works on topics that you are interested in. Pick a person whose papers you like or admire.
(10) Apply to several PhD programs in the schools of the above-mentioned professors and mention in your letter that you’d like to work with that professor but would be open to work with others.
(11) ask your undergrad professor to write a recommendation letter for you. It’s maximally efficient if your undergrad professor is known by your favorite PhD advisor.
(12) if you don’t get accepted in one of your favorite PhD programs, get a job at Facebook or Google and try to get a gig as an engineer assisting research scientists at FAIR or Google Brain.
(13) publish a papers with the research scientists in question. Then re-apply to PhD programs and ask the FAIR or Google scientists you work with to write a recommendation letter for you.
https://www.quora.com/What%E2%80%99s-your-advice-for-undergraduate-student-who-aspires-to-be-a-research-scientist-in-deep-learning-or-related-field-one-day
#machine_learning
Quora
What’s your advice for undergraduate student who aspires to be a research scientist in deep learning or related field one day?
Answer (1 of 8): * (0) take all the continuous math and physics class you can possibly take. If you have the choice between “iOS programming” and “quantum mechanics”, take “quantum mechanics”. In any case, take Calc I, Calc II, Calc III, Linear Algebra,…
Machine Learning & Computational Statistics Course
Course Intro: This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice.
https://davidrosenberg.github.io/ml2016/#home
#machine_learning #statistics #course
Course Intro: This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice.
https://davidrosenberg.github.io/ml2016/#home
#machine_learning #statistics #course