Going deeper into Deep CNNs through visualization methods: Saliency maps, optimize a random input image and deep dreaming with Keras

Baby Health model made in Python.

This project is an aspect of a big project that is called the Self-Driving Car. One of the essential techniques in Self-Driving Car engineering is detecting the Traffic Sign. In this project I have used Deep Learning for recognizing the Traffic Signs.

I have prepared a Deep Convolutional Generative Adversarial Networks here.

This projects presents a technique for Chromatic Dispersion compensation, based on an optimized filter.

Convolution Layer 구현 (CNN)

Image Classification of Best Location from Earth

Fashion MNIST classification using PyTorch

Train car on a known track to generate dataset which include steering angle and view of car from 3 different angles. Use this dataset to drive car on an unknown track. And also learn to identify 43 different traffic signals using existing dataset.

Image recognition and classification based on Convolutional Neural Networks to identify drawings of animals