Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
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Feb 1, 2022 - HTML
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Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
Powerful modern math library for PHP: Features descriptive statistics and regressions; Continuous and discrete probability distributions; Linear algebra with matrices and vectors, Numerical analysis; special mathematical functions; Algebra
The Machine Learning & Deep Learning Compendium was a list of references in my private & single document, which I curated in order to expand my knowledge, it is now an open knowledge-sharing project compiled using Gitbook.
Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.
The basic distribution probability Tutorial for Deep Learning Researchers
Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
Algorithm is a library of tools that is used to create intelligent applications.
Teaching Materials for Dr. Waleed A. Yousef
General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python
数学知识点滴积累 矩阵 数值优化 神经网络反向传播 图优化 概率论 随机过程 卡尔曼滤波 粒子滤波 数学函数拟合
Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"
Pytorch implementations of density estimation algorithms: BNAF, Glow, MAF, RealNVP, planar flows
The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning , Deep Learning and other fields of AI
VIP cheatsheets for Stanford's CME 106 Probability and Statistics for Engineers
A C++ header-only library of statistical distribution functions.
Self-study on Larry Wasserman's "All of Statistics"
Rather than trying to rebuild all functionality from Distributions.jl, we're first focusing on reimplementing logdensity (logpdf in Distributions), and delegating most other functions to the current Distributions implementations.
So for example, we have
distproxy(d::Normal{(:μ, :σ)}) = Dists.Normal(d.μ, d.σ)This makes some functions in Distributions.jl available through
Quantitative Interview Preparation Guide, updated version here ==>
To know stats by heart
Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].
A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD theses, articles and open-source libraries.
Generate realizations of stochastic processes in python.
A curated list of mathematics documents ,Concepts, Study Materials , Algorithms and Codes available across the internet for machine learning and deep learning
Probabilidad y variables aleatorias para ML con R y Python con los Drs R.Alberich y A.Mir
My Solutions to 120 commonly asked data science interview questions.
Courses, Articles and many more which can help beginners or professionals.
Add a description, image, and links to the probability topic page so that developers can more easily learn about it.
To associate your repository with the probability topic, visit your repo's landing page and select "manage topics."
I found several useful applications of pseudo-random number sampling in the past. In particular:
(This issue serves a reminder to add the respective methods. Pull requests always welcome.)