Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural networks are a type of deep learning, which is a type of machine learning. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and natural language processing. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and natural language processing.
Deepfakes Software For All
Open standard for machine learning interoperability
DeepFaceLab is the leading software for creating deepfakes.
pix2code: Generating Code from a Graphical User Interface Screenshot
A paper list of object detection using deep learning.
Turn your two-bit doodles into fine artworks with deep neural networks, generate seamless textures from photos, transfer style from one image to another, perform example-based upscaling, but wait... there's more! (An implementation of Semantic Style Transfer.)
An open source library for deep learning end-to-end dialog systems and chatbots.
Synthesizing and manipulating 2048x1024 images with conditional GANs
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
This repository is a curated collection of links to various courses and resources about Artificial Intelligence (AI)
Torchreid: Deep learning person re-identification in PyTorch.
LPIPS metric. pip install lpips
DeepFill v1/v2 with Contextual Attention and Gated Convolution, CVPR 2018, and ICCV 2019 Oral
Scalable and user friendly neural 🧠 forecasting algorithms.
PyTorch implementation of Super SloMo by Jiang et al.
JAX-based neural network library
Flops counter for convolutional networks in pytorch framework
PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
computer vision and sports