Official code for ResUNetplusplus for medical image segmentation (TensorFlow & Pytorch implementation)

Basic Gesture Recognition Using mmWave Sensor - TI AWR1642

Benchmarks across Deep Learning Frameworks in Julia and Python

2D Convolutional Recurrent Neural Networks implemented in PyTorch

Numpy implementation of deep learning

layers

Kaggle Machine Learning Competition Project : In this project, we will create a classifier to classify fashion clothing into 10 categories learned from Fashion MNIST dataset of Zalando's article images

This code uses the pyTorch Conv2D modules to make the PIV algorithms work faster on GPU

Mokka is a minimal Inference Engine for Dense and Convolutional 2D Layer Neural Networks. Written on a single C++ header, it uses AVX2

CNN

Basic_CNN_Implementation

A project to perform people identification at a distance using face and gait data with deep learning

Basic_CNN_Implementation

This PyTorch-based project implements a deep neural network for multi-class classification of fashion items. The dataset consists of images categorized into three classes: glasses vs. sunglasses, shoes, and trousers vs. jeans.

In this project, we will create a classifier to classify fashion clothing into 10 categories learned from Fashion MNIST dataset.

A FAST pure numpy based 1D, 2D, even n-dimensional convolution library.

Convolutional Neural Network to Classify Dogs and Cat. I built a ImageClassifier which classifies and tells you whether its a Dog image or a Cat image. I built a convolutional network which consists of Three Convolution layer and Three MaxPooling layer. Each Convolutional layer has filters, kernel size. Maxpooling layer has stride and pooling size. Then this Convolutional layer Connects to DeepNeuralNetwork. DNN has three hidden layer and output layer having Sigmoid Activation function. I trained this model for 31 epochs and achieved an accuracy of around 85%. I found this massive image dataset online which has 10,028 images(Ten Thousand and Twenty Eight). My model Predicted accurately during the testing phase. I even tested my model using my neighbor dog's pic and it predicted accurately.

Reinforcement Learning with Actor-Critic to play Breakout-v4 (Atari) from OpenAI Gym

A repository for machine learning problems and exploration of different ML libraries. The goal of this repository is to collect takeaways while developing ML models. This should improve my overall understanding of developing machine learning applications.

This project aims to develop an advanced DL model using CNN to accurately detect and classify brain tumors from MRI scans.

Creating a classifier for the German Traffic Signs dataset that classifies images of traffic signs into 43 classes.

Image classification based computer vision model CNN

The "witin_nn" framework, based on PyTorch, maps neural networks to chip computations and supports operators including Linear, Conv2d, and GruCell. It enables 8-12 bit quantization for inputs/outputs and weights, implementing QAT.

Deployed the super-resolution convolution neural network (SRCNN) using Keras. Recovers a high-resolution image from a low-resolution input.

No description provided.

IMAGE CLASSIFICATION USING CNN and MobileNetV3

Image Augmentation

Keras Convolutianl Networks

Utilized CNN models to classify images of mountains and forests, treating mountains as the positive class and forests as the negative class. We compare the performance of a pre-trained model, a custom CNN model, and a CNN model with data augmentation.

Submission atau Proyek Akhir yang dilakukan ini bertujuan untuk melakukan klasifikasi gambar tingkat lanjut dari kelas Belajar Penerapan Data Science yang diberikan oleh Dicoding dan IDCamp.