Implementing Neural Network from scratch (MLP and CNN), purely in numpy with optimizers
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Updated
Apr 27, 2022 - Python
Implementing Neural Network from scratch (MLP and CNN), purely in numpy with optimizers
ConvNN is used to predict digits for the MNIST dataset
This is a reposatory for implementation of different types of optimizers (SGD, RMSprop, Adam etc.) with three different use cases Function Approximation, Multi-class Single-label Classification and Multi-class Multi-label Classification)
This GitHub repository contains the code used for CS-671: Introduction to Deep Learning course offered by IIT Mandi during the Even Semester of 2022. The repository includes the implementations of various deep learning algorithms and techniques covered in the course.
Code to conduct experiments for the paper Modified Gauss-Newton method for solving a smooth system of nonlinear equations.
An electrical grid simulator to calculate the least grid cost using optimizers from nevergrad package.
implementation of sophia (Second-Order cliPped stocHastic optimizAtion)
Tutorials on optimizers for deep neural networks
0th order optimizers, gradient chaining, random gradient approximation
Embedding
Cognitive bias inspired GLVQ optimizers
This repository contains about final project for bachelor degree
Code to conduct experiments for the paper Regularization and acceleration of Gauss-Newton method.
Our objective is Classification of Citrus Leaves Data using CNN classifier. Here, we are comparing performances of different optimizers and hyper-parameters on the basis of different metrics like Accuracy, Precision, Recall.
Polyvalent neural network
Neural networks framework built from scratch on Python.
Uptodate fork for visualizing the loss landscape of neural nets
A bridge from call back to iterator Acronym: bluesky callback iterator bridge. Motivated that bluesky wants to iterate over plans while solvers typically use call backs
A project made for understanding how to construct CNN models from the very basics and how to choose various combinations of hyperparameters to improve accuracy of predictions
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