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🧠 Deep Learning with PyTorch

A clean and modular deep learning project built using PyTorch. This repo is designed to be beginner-friendly and scalable for more complex tasks in the future.


🚀 Features

  • Modular PyTorch pipeline
  • Dataset loaders and transformations
  • Custom model architectures
  • Training and validation loops
  • Checkpoint saving & loading
  • Logging with tqdm
  • GPU support

🗂️ Project Structure

deep-learning-pytorch/ │ ├── data/ # Datasets or data loading scripts ├── models/ # Custom model architectures ├── utils/ # Helper functions (metrics, plotting, etc.) ├── checkpoints/ # Saved model weights ├── train.py # Training loop ├── evaluate.py # Evaluation script ├── config.py # Configuration (hyperparameters, paths) └── requirements.txt # All dependencies


📊 Models Included

Model Description
CNN Simple convolutional network
MLP Multi-layer perceptron
ResNet (custom) Residual block architecture

🏁 Getting Started

1. Clone the Repository

git clone https://github.com/Abisheck007/PyTorch_Deeplearning.git
cd deep-learning-pytorch

Hyper parameteres tuning
python train.py --lr 0.001 --epochs 20 --model mlp

About

This repository showcases a Deep Learning project developed with PyTorch, designed to address the classification of dl models and It utilizes the flexibility and efficiency of the PyTorch framework for building, training, and evaluating deep neural networks

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