Handwritten Multi-digit String Segmentation and Recognition using Deep Learning.

Deep neural networks and convolutional neural networks to classify German traffic signs.

I am working on implementing Machine Learning Algorithms from scratch.

Contains python files to break simple captchas.

A python script to convert GIF into a Google's DeepDream style GIFs.

My Submission For Udacity Self-Driving Car Engineer Nanodegree Program Traffic Sign Classifier Project

Migrating from Theano to Tensorflow using Lenet as a case study.

Implementation of LeNet-5 over MNIST Dataset using PyTorch from Scratch, presenting an accuracy of ~99%

Project: Build a Traffic Sign Recognition Program

Some basic CNN-based games controlled by hand poses.

Implementing the LeNet-5 neural network architecture to classify MNIST Digits

This project utilizes a convolutional network to identify 9 different kinds of skin cancers including melanoma, nevus, and more. The model is trained on over 2,200 pictures of various skin cancers based off of this dataset. This model implements fundamental computer vision and classification techniques and includes a step-by-step implementation.

Building & Deploying Computer Vision Models

A convolutional model for recognition of handwritten digits, plus, the driver program for the same.

Traffic Sign Classifier using the German Traffic Sign Dataset

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

I've developed a model using a Convolutional Neural Network (CNN) to classify traffic signs using LeNet architecture

The Deep Learning Concepts Repository is a concise and accessible collection of essential concepts in deep learning. It provides clear explanations and examples for neural networks, CNNs, RNNs, activation functions, loss functions, backpropagation, gradient descent, and overfitting/underfitting. An invaluable resource for beginners and practitioner

This is a machine learning project where I utilized the LeNet-5 architecture to create a convolutional deep network that classifies 43 different kind of traffic signs. I've made sure to include a full step-by-step implementation of the project as well as detailed notes for every step.

ConvNet implementation for CIFAR-10 dataset using pytorch