This repository contains the code for all the programming tasks of the Mathematics for Machine Learning courses taught at Coursera by Imperial College London
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Updated
Sep 8, 2018 - Jupyter Notebook
This repository contains the code for all the programming tasks of the Mathematics for Machine Learning courses taught at Coursera by Imperial College London
Explanation of Mathematics used in Machine Learning Algorithms and some Projects
Blank notebooks and solutions for the Mathematics for Machine Learning specialization by Imperial College of London on Coursera.
Численные методы оптимизации реализованные для курса ВолгГТУ "Методы оптимизации"
Official Repository for Pydata Pune Meetup - Gang Up -I
This repository consists of my assignment submissions for Mathematics in machine Learning course by Imperial College on Coursera.
Mathematical consequences of orthogonal weights initialization and regularization in deep learning. Experiments with gain-adjusted orthogonal regularizer on RNNs with SeqMNIST dataset.
Repo contains miscellaneous programs
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.
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.
Awesome resources for learning, practicing, and researching Machine Learning.
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
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.
In this repository, I have uploaded PDF of mathematics that are required for data science which include linear algebra, statistics, differential calculus. You can either learn some PDF for that particular day or go to the final PDF to get everything needed.
Data Science With Python->Target: AI/ML
My Collection of Certificates!
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