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.

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

Generalization of Adam, AdaMax, AMSGrad algorithms for PyTorch

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

Create animated videos for various optimizers used for training deep learning models

Deep Learning Optimizers

Investigating the Behaviour of Deep Neural Networks for Classification

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

traffic sign detection using ML

Collection of notebooks I made on deep learning topics.

Analyze the performance of 7 optimizers by varying their learning rates

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