There are 11 repositories under gradient-descent topic.
Fast and Easy Infinite Neural Networks in Python
OptimLib: a lightweight C++ library of numerical optimization methods for nonlinear functions
Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.
A TensorFlow-inspired neural network library built from scratch in C# 7.3 for .NET Standard 2.0, with GPU support through cuDNN
🧩 Shape-Safe Symbolic Differentiation with Algebraic Data Types
Image morphing without reference points by applying warp maps and optimizing over them.
The simplest form of an artificial neural network explained and demonstrated.
This repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.
A highly customizable SQP & barrier solver for nonlinearly constrained optimization
NMFLibrary: Non-negative Matrix Factorization (NMF) Library: Version 2.1
Implementing Multiple Layer Neural Network from Scratch
A PyTorch implementation of Learning to learn by gradient descent by gradient descent
Implementation of basic ML algorithms from scratch in python...
This is a list of peer-reviewed representative papers on deep learning dynamics (optimization dynamics of neural networks). The success of deep learning attributes to both network architecture and stochastic optimization. Thus, deep learning dynamics play an essentially important role in theoretical foundation of deep learning.
Six snippets of code that made deep learning what it is today.
👩🏻⚕️Covid-19 estimation and forecast using statistical model; 新型冠状病毒肺炎统计模型预测 (Jan 2020)
A curated list of mathematics documents ,Concepts, Study Materials , Algorithms and Codes available across the internet for machine learning and deep learning
Codes and Project for Machine Learning Course, Fall 2018, University of Tabriz
Machine learning beginner to Kaggle competitor in 30 days. Non-coders welcome. The program starts Monday, August 2, and lasts four weeks. It's designed for people who want to learn machine learning.
Code and data for Neural Holography
This Repository contains Solutions to the Quizes & Lab Assignments of the Machine Learning Specialization (2022) from Deeplearning.AI on Coursera taught by Andrew Ng, Eddy Shyu, Aarti Bagul, Geoff Ladwig.
Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language
Differentiable RayTracing in Julia
Lesson material on data science and machine learning topics/concepts
numerical optimization in pytorch
A neural network library written from scratch in Rust along with a web-based application for building + training neural networks + visualizing their outputs
Projects from the Deep Learning Specialization from deeplearning.ai provided by Coursera
This repo has been created to share the solutions of all the quizzes and assignments of all three courses of this specialization.
My workshop on machine learning using python language to implement different algorithms
An example project using a feed-forward neural network for text sentiment classification trained with 25,000 movie reviews from the IMDB website.
Some experiments about Machine Learning
Gathers Machine learning models using pure Numpy to cover feed-forward, RNN, CNN, clustering, MCMC, timeseries, tree-based, and so much more!