SwiftUI sample app using Clean Architecture. Examples of working with SwiftData persistence, networking, dependency injection, unit testing, and more.
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SwiftUI sample app using Clean Architecture. Examples of working with SwiftData persistence, networking, dependency injection, unit testing, and more.
YTKNetwork is a high level request util based on AFNetworking.
Your always-on AI coworkers that move between text, calls, and Slack.
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility
Android Asynchronous Networking and Image Loading
Recurrent Neural Network - A curated list of resources dedicated to RNN
A scikit-learn compatible neural network library that wraps PyTorch
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
Design-first Go framework that generates API code, documentation, and clients. Define once in an elegant DSL, deploy as HTTP and gRPC services with zero drift between code and docs.
Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (ICLR 2020)
header only, dependency-free deep learning framework in C++14
Event-driven networking engine written in Python.
Recurrent neural network for audio noise reduction
🚀 A Complete Fast Android Networking Library that also supports HTTP/2 🚀
Gorgonia is a library that helps facilitate machine learning in Go.
Download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.
Emulator for rapid prototyping of Software Defined Networks
Remote network and data debugging for your native iOS app using Chrome Developer Tools
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
Easy to use CFNetwork wrapper for HTTP requests, Objective-C, Mac OS X and iPhone
Convolutional Neural Network for Text Classification in Tensorflow
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
Speed up downloads by combining multiple network connections in parallel
A private network system that uses WireGuard under the hood.