The project aimed to implement Deep NN / RNN based solution in order to develop flexible methods that are able to adaptively fillin, backfill, and predict time-series using a large number of heterogeneous training datasets.

[Python] [arXiv/cs] Paper "An Overview of Gradient Descent Optimization Algorithms" by Sebastian Ruder

Nadir: Cutting-edge PyTorch optimizers for simplicity & composability! 🔥🚀💻

The optimization methods in deep learning explained by Vietnamese such as gradient descent, momentum, NAG, AdaGrad, Adadelta, RMSProp, Adam, Adamax, Nadam, AMSGrad.

A comparison between implementations of different gradient-based optimization algorithms (Gradient Descent, Adam, Adamax, Nadam, Amsgrad). The comparison was made on some of the most common functions used for testing optimization algorithms.

Generalization of Adam, AdaMax, AMSGrad algorithms for PyTorch

Classification of data using neural networks — with back propagation (multilayer perceptron) and with counter propagation

Dataset, and codes on the manuscript titled “Enhanced Orange Fruit Disease Detection Via An Adamax-Optimized Yolov11 Nano In Precision Agriculture”

A deep learning classification program to detect the CT-scan results using python

Repo for all animations created for the ML, DL and LLM courses

Deep Learning Optimizers

Course from O. Wintenberger for Master M2A at Sorbonne University : Online Convex Optimization

Investigating the Behaviour of Deep Neural Networks for Classification

Collection of notebooks I made on deep learning topics.

traffic sign detection using ML

Analyze the performance of 7 optimizers by varying their learning rates

A deep learning classification program to detect the CT-scan results using python