Search Results for "neural network visualiser"

ncnn is a high-performance neural network inference computing framework designed specifically for mobile platforms. It brings artificial intelligence right at your fingertips with no third-party dependencies, and speeds faster than all other known open source frameworks for mobile phone cpu. ncnn allows developers to easily deploy deep learning algorithm models to the mobile platform and create intelligent APPs.

XNNPACK is a highly optimized, low-level neural network inference library developed by Google for accelerating deep learning workloads across a variety of hardware architectures, including ARM, x86, WebAssembly, and RISC-V. Rather than serving as a standalone ML framework, XNNPACK provides high-performance computational primitives—such as convolutions, pooling, activation functions, and arithmetic operations—that are integrated into higher-level frameworks like TensorFlow Lite, PyTorch Mobile, ONNX Runtime, TensorFlow.js, and MediaPipe. ...

...A data science layer adds NumPy-style reshaping and generators, Pandas-style numeric CSV, functional utils, and SciKit-Learn-style preprocessing (one-hot encoding, train/test split). An AI/ML layer adds activations, softmax, MSE loss, and numerical gradients for neural-network forward passes. An ultimate layer tops the scientific stack off with computational backend selection (CPU / AVX SIMD / GPU stub), a Micrograd-style scalar autograd engine.

iOS-10-Sampler is an example application that collects practical demonstrations of APIs introduced with iOS 10. It was created for Xcode 8 and requires a physical iOS device because some examples use Metal. The project covers speech recognition, looped video playback, Live Photo capture, and audio volume fading. Its graphics and machine learning samples use Metal Performance Shaders for handwritten digit detection and real-time image recognition. Interface examples demonstrate...

TNN, a high-performance, lightweight neural network inference framework open sourced by Tencent Youtu Lab. It also has many outstanding advantages such as cross-platform, high performance, model compression, and code tailoring. The TNN framework further strengthens the support and performance optimization of mobile devices on the basis of the original Rapidnet and ncnn frameworks.

Bender allows you to easily define and run neural networks on your iOS apps, it uses Apple’s MetalPerformanceShaders under the hood. Bender provides the ease of use of CoreML with the flexibility of a modern ML framework. Bender allows you to run trained models, you can use Tensorflow, Keras, Caffe, the choice is yours. Either freeze the graph or export the weights to files. You can import a frozen graph directly from supported platforms or re-define the network structure and load the weights. ...