Object detection learning path

A simple CNN classifier example for PyTorch beginners.

A repository to document my PyTorch learning journey for deep learning. Includes fundamental concepts, hands-on examples, model building, training, and optimization. Features implementations of neural networks, CNNs, RNNs, and more, making it beginner-friendly and practical.

This repository serves as a beginner-friendly guide to PyTorch fundamentals and Convolutional Neural Networks (CNNs). It provides: An introduction to PyTorch basics: tensors, operations, autograd, and more. Hands-on examples for building, training, and validating CNN models.

Transfer learning with VGG11 on CIFAR-10 using PyTorch. Includes model customization, data preprocessing, learning rate finder, fine-tuning, evaluation, and visualization. A practical and educational notebook for beginners in deep learning and CNNs.

Basic custom CNN for MNIST dataset classification using PyTorch. If you are getting started with pytorch and want to get some elementary example, this notebook is for you :)

Train a CNN for image classification (hymenoptera) using transfer learning (based on https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html)

This repository is a collection of PyTorch code examples, covering beginner to advanced topics, and including implementation of CNN models from scratch.

🧠 A beginner-friendly PyTorch project that teaches how image classification works by training a CNN to distinguish cats from dogs. Built for learning, not production.

Educational deep learning models built using PyTorch — including CNNs, classifiers, and neural networks. Ideal for beginners and self-learners looking to understand how models work from scratch.

A lightweight project that implements a simple convolutional neural network (CNN) for image classification tasks using PyTorch. This project is designed for beginners to learn the basics of deep learning, including training and evaluating CNN models on datasets like CIFAR-10

This repository is intended to help beginners and juniors understand how to implement CNN-based image classification in PyTorch. It can be a useful starting point or reference for your own projects.

Handwritten Digit Recognizer (PyTorch) A beginner-friendly Convolutional Neural Network (CNN) built with PyTorch to recognize handwritten digits using the MNIST dataset. This project demonstrates the full machine learning pipeline: from data preprocessing and model architecture design to real-world testing with custom JPEG images.

A PyTorch CNN that classifies memes as Vulgar or Non-Vulgar, with custom dataset handling, image preprocessing, Adam optimizer training, and evaluation using accuracy, precision, recall, and F1-score. Beginner-friendly for binary image classification and content moderation.

A collection of beginner-friendly Deep Learning models built with TensorFlow, Keras, and PyTorch. Covers CNNs, RNNs, LSTMs, GANs, Autoencoders, and Transformers. Includes image, text, audio, and time-series tasks with Colab-ready notebooks, datasets, results, and documentation for easy learning and showcasing.

Explore stop sign detection using PyTorch and CV Studio in this GitHub repository. Leverage transfer learning for efficient CNN training. The guide provides step-by-step instructions, code, and datasets, making it accessible for both beginners and experts. Enhance your computer vision skills to accurately identify stop signs in diverse images.

A beginner-friendly PyTorch CNN project on FashionMNIST dataset, with CSV loading, training, evaluation, and visualization

Deep learning project for Khmer handwritten character recognition using CNNs in PyTorch, designed for beginners and educational purposes

Beginner deep learning project using PyTorch to build and train a CNN for image classification on the CIFAR-10 dataset.

A beginner-friendly PyTorch project transitioning from a simple Linear Neural Network to a high-accuracy Convolutional Neural Network (CNN) using the MNIST dataset.

A beginner-friendly PyTorch image classification project using CNNs on the CIFAR-10 dataset. Trains, evaluates, and visualizes predictions for real-world computer vision tasks.

Image Classifier with PyTorch — trains a CNN on CIFAR-10 to classify 10 image categories. Includes training and inference scripts. Great for deep learning beginners.

This is a CNN tutorial for beginners about a digits recognition model trained on the MNIST dataset. I built two models with TensorFlow/Keras and PyTorch/Skorch respectively.

A practical guide to deep learning with PyTorch. This repo includes beginner-friendly tutorials, model implementations (CNNs, RNNs, etc.), custom datasets, training loops, and real-world projects in CV and NLP. Perfect for learning and building with PyTorch.

A lightweight PyTorch module that provides pre-implemented, customizable neural network templates (Linear, Binary Classifier, Multi-Class Classifier, CNN) for quick prototyping and training. Ideal for beginners or rapid experimentation.

This repository demonstrates a simple and effective implementation of a Convolutional Neural Network (CNN) using PyTorch to classify handwritten digits from the MNIST dataset. The project is beginner-friendly and serves as a great introduction to deep learning and image classification using CNNs.

A beginner-friendly Computer Vision project using PyTorch to recognize handwritten digits. Features a custom CNN architecture, real-time training visualization, and model export for Android integration. Perfect for learning Tensor transformations and Deep Learning fundamentals. 🚀 #PyTorch #AI #ComputerVision

The PyTorch-Deep-Learning repository offers practical notebooks for learning deep learning with PyTorch, covering key topics like tensors, CNNs, and model building. It's a resource for beginners to intermediate learners, providing examples for tasks such as image classification and dataset handling.

Hands-on Computer Vision projects using Python, OpenCV, NumPy, and PyTorch. Covers image processing, feature extraction, deep learning (CNNs), and object detection — with practical notebooks, code samples, and end-to-end implementations for beginners.

This project is designed for beginners in Deep Learning. We implement a basic Convolutional Neural Network (CNN) to classify handwritten digits (0-9) using the MNIST dataset in two major frameworks (PyTorch and TensorFlow/Keras).