Educational deep learning library in plain Numpy.
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Updated
Jun 21, 2022 - Python
Educational deep learning library in plain Numpy.
A collection of various gradient descent algorithms implemented in Python from scratch
[Python] [arXiv/cs] Paper "An Overview of Gradient Descent Optimization Algorithms" by Sebastian Ruder
Neural Networks and optimizers from scratch in NumPy, featuring newer optimizers such as DemonAdam or QHAdam.
Package used for mathematical optimization.
NAG-GS: Nesterov Accelerated Gradients with Gauss-Siedel splitting
ISANet is a Neural Network Library.
ASCPD: an accelerated algorithm for CPD
Hands on implementation of gradient descent based optimizers in raw python
Digit recognition neural network using the MNIST dataset. Features include a full gui, convolution, pooling, momentum, nesterov momentum, RMSProp, batch normalization, and deep networks.
Repository with the submissions for the 'Fundamentals of Optimization' course, where techniques such as gradient descent and its variants are implemented. These include gradient descent with a fixed step size (alpha), Nesterov GD with a fixed step, GD with a decreasing step size, GD with diagonal scaling and fixed step size.
Assignment submission for the course Fundamentals of Deep Learning (CS6910) in the Spring 2022 Semester, under Prof. Mitesh Khapra
🧠Implementation of a Neural Network from scratch in Python for the Machine Learning Course.
SVM algorithms implementation from scratch for AI539 class project
Implementation of SVD without using package.
This repository contains various tools of machine learning like the GRADIENT DESCENT and others, implemented from scratch using packages like numPy, matplotlib, pandas
"Simulations for the paper 'A Review Article On Gradient Descent Optimization Algorithms' by Sebastian Roeder"
Primal-Dual algorithm for smooth regularization of non-smooth optimization functions
Implemented optimization algorithms, including Momentum, AdaGrad, RMSProp, and Adam, from scratch using only NumPy in Python. Implemented the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimizer and conducted a comparative analysis of its results with those obtained using Adam.
From linear regression towards neural networks...
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