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bnn

Training and Implementation of a CNN for image classification with binary weights and activations on FPGA with HLS tools

cnn-acceleration-using-fpga

Aim of this Research is to optimise and Accelerate the Convolutional Neural Networks and show how FPGAs offer significant efficiency improvements over CPU and GPU.

deepcabac

dockerlabs

Docker - Beginners | Intermediate | Advanced

hands-on-machine-learning-with-cpp

Hands-On Machine Learning with C++, published by Packt

image-classification-using-residual-networks

> In theory, very deep networks can represent very complex functions; but in practice, they are hard to train. Residual Networks, allow you to train much deeper networks than were previously practically feasible. Image Classification model was built using ResNets. Model was trained on SIGNS dataset. This was done using Keras.

jetson

Helmut Hoffer von Ankershoffen experimenting with arm64 based NVIDIA Jetson (Nano and AGX Xavier) edge devices running Kubernetes (K8s) for machine learning (ML) including Jupyter Notebooks, TensorFlow Training and TensorFlow Serving using CUDA for smart IoT.

ncnn

ncnn is a high-performance neural network inference framework optimized for the mobile platform

nn_playground

Experimental keras implementation of novel neural network structures

nvidia_e2e

RAPIDS Workshop

onnx

Open standard for machine learning interoperability

pynq-computervision

Computer Vision Overlays on Pynq

squeezenet-deep-compression

tensorflow-examples

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

tensorrt_demos

Some examples demonstrating how to optimize caffe/tensorflow/darknet models with TensorRT and run real-time inferencing with the optimized TensorRT engines

zc702_dpu140_trd

TRD of DPU v1.4.0 @ Xilinx zc702 board