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learning-rate-scheduling

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This project conducts a thorough analysis of weather time series data using diverse statistical and deep learning models. Each model was rigorously applied to the same weather time series data to assess and compare their forecasting accuracy. Detailed results and analyses are provided to delineate the strengths and weaknesses of each approach.

  • Updated May 25, 2024
  • Jupyter Notebook

In this repository, I put into test my newly acquired Deep Learning skills in order to solve the Kaggle's famous Image Classification Problem, called "Dogs vs. Cats".

  • Updated May 7, 2022
  • Python

End-to-end Image Classification using Deep Learning toolkit for custom image datasets. Features include Pre-Processing, Training with Multiple CNN Architectures and Statistical Inference Tools. Special utilities for RAM optimization, Learning Rate Scheduling, and Detailed Code Comments are included.

  • Updated Nov 14, 2023
  • Jupyter Notebook

Comprehensive image classification for training multilayer perceptron (MLP), LeNet, LeNet5, conv2, conv4, conv6, VGG11, VGG13, VGG16, VGG19 with batch normalization, ResNet18, ResNet34, ResNet50, MobilNetV2 on MNIST, CIFAR10, CIFAR100, and ImageNet1K.

  • Updated Oct 5, 2021
  • Python

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