Linear Algebra; Multivariate Calculus; Optimization; Probability and Statistics; Phenomena in High Dimensions; Approximation Theory; Functional Analysis, etc..
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Updated
Jan 11, 2020
Linear Algebra; Multivariate Calculus; Optimization; Probability and Statistics; Phenomena in High Dimensions; Approximation Theory; Functional Analysis, etc..
Replay Buffer is a blog on machine learning, computer science and mathematics.
Data Science With Python->Target: AI/ML
Personal Research on the mathematical foundation of CNNs as well as my first computer vision problem
Companion webpage to the book "Mathematics For Machine Learning"
My Collection of Certificates!
I)Testing and practicing different machine learning algorithms II) Mathematical modeling and simulation algorithms.
A walk through of the creation of a deep neural network from scratch!
My personal notes for learning about programming and math (in particular statistics). So far it's mostly just my solutions to book exercises, maybe I'll write up some things later on. This repo consists of my studying after leaving university, I won't upload the notes from before that.
MATHEMATICS FOR MACHINE LEARNING Coursera Specialization by IMPERIAL COLLEGE LONDON
Mathematics for Machine Learning Computer Project
Assignments for lesson: Pattern Recognition - Machine Learning (UOA)
Research project on random forests and how can one improve their prediction accuracy for the NASA airfoil learning problem using mathematical analysis
Notes taken during studying mathematics and machine learning
Repo contains miscellaneous programs
These are the handwritten notes on, how to set up an IDE, an introduction to Conda environment, how to use Git and GitHub for version control, and mathematics for data science.
This repository contains a comprehensive collection of mathematical concepts and techniques relevant to various fields of AI, including ML, DL & other areas.It also includes the corresponding source code for all programming tasks associated with the Mathematics for Machine Learning courses, which are taught at Coursera by Imperial College London.
Go simple-to-use plotting library that includes visualization of vectors and matrices, the Gaussian distribution, time series and seasonality and more. It offers statistical components of datasets, such as the variance and covariance.
Anonymous Data Scientist
Notes from Mathematics for Machine Learning and Data Science Specialization
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