AI2018

UserBenchmark
UserBenchmark AMD-Ryzen-TR-2990WX-vs-Intel-Core-i9-7960X
UserBenchmark AMD-Ryzen-7-2700X-vs-Intel-Core-i7-8700K
UserBenchmark AMD-Ryzen-7-2700X-vs-AMD-Ryzen-7-1800X
UserBenchmark AMD-Ryzen-7-2700X-vs-Intel-Core-i7-4790K
UserBenchmark Intel-Core-i7-4790K-vs-Intel-Core-i5-4690
UserBenchmark Intel-Core-i5-4670K-vs-Intel-Core-i7-4790K
UserBenchmark AMD-Ryzen-7-2700X-vs-Intel-Core-i7-3770K
UserBenchmark Intel-Core-i5-3570-vs-AMD-Ryzen-7-2700X
UserBenchmark Intel-Core-i5-3570-vs-Intel-Core-i7-3770K
UserBenchmark Intel-Core-i5-3570-vs-Intel-Core-i5-4690
UserBenchmark Intel-Core-i5-3570-vs-Intel-Core-i5-4670K
UserBenchmark Intel-Core-i5-3570-vs-Intel-Core-i7-4790K

ROCm Software Platform
Deep Learning on ROCm ROCm Tensorflow Release
HIP : Convert CUDA to Portable C++ Code
RadeonOpenCompute/ROCm ROCm — Open Source Platform for HPC and Ultrascale GPU Computing https://rocm.github.io/
ROCmSoftwarePlatform pytorch
ROCmSoftwarePlatform/MIOpen
Welcome to MIOpen Advanced Micro Devices, Inc’s open source deep learning library.
AMD ROCm GPUs now support TensorFlow v1.8, a major milestone for AMD’s deep learning plans
ROCm-Developer-Tools/HIP HIP : Convert CUDA to Portable C++ Code
hipCaffe Quickstart Guide Install ROCm
Half-precision floating point library
convnet-benchmarks
computeruniverse.ru VEGA

Deep Learning on ROCm Announcing our new Foundation for Deep Learning acceleration MIOpen 1.0 which introduces support for Convolution Neural Network (CNN) acceleration — built to run on top of the ROCm software stack!
MXNet is a deep learning framework that has been ported to the HIP port of MXNet. It works both on HIP/ROCm and HIP/CUDA platforms. Mxnet makes use of rocBLAS,rocRAND,hcFFT and MIOpen APIs.
ROCmSoftwarePlatform/hipCaffe
ROCm Software Platform
«Radeon Open Compute (ROCm) — это новая эра для платформ расчета на GPU, призванных использовать возможности ПО с открытым исходным кодом, чтобы реализовать новые решения для высокопроизводительных и гипермасштабируемых вычислений. ПО ROCm дает разработчикам абсолютную гибкость в том, где и как они могут использовать GPU-вычисления.
MIOpen
A Comparison of Deep Learning Frameworks

Сравнение Google TPUv2 и Nvidia V100 на ResNet-50
AI accelerator
eSilicon deep learning ASIC in production qualification
Бенчмарк нового тензорного процессора Google для глубинного обучения
Специализированный ASIC от Google для машинного обучения в десятки раз быстрее GPU
В MIT разработали фотонный чип для глубокого обучения
Machine Learning Series
Visual Computing Group
source{d} tech talks — Machine Learning 2017
10 Alarming Predictions for Deep Learning in 2018
Эксперименты с malloc и нейронными сетями
LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks
HiPiler: Visual Exploration Of Large Genome Interaction Matrices With Interactive Small Multiples
Deep Learning Hardware Limbo
OpenAI
Inside OpenAI
Math Deep learning
Facebook and Microsoft introduce new open ecosystem for interchangeable AI frameworks

Inside AI Next-level computing powered by Intel AI Intel® Nervana™ Neural Network Processor

Intel® Nervana™ Neural Network Processor: Architecture Update Dec 06, 2017
AI News January 2018
Andrej Karpathy

MIT 6.S094: Deep Reinforcement Learning for Motion Planning

RI Seminar: Sergey Levine : Deep Robotic Learning

Tim Lillicrap — Data efficient deep reinforcement learning for continuous control
Intermediate Python
Tensors and Dynamic neural networks in Python with strong GPU acceleration http://pytorch.org
Tutorial for beginners https://github.com/GunhoChoi/Kind-PyTorch-Tutorial
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
PyTorch documentation
Transfering a model from PyTorch to Caffe2 and Mobile using ONNX
ONNX is a new open ecosystem for interchangeable AI models.
Open Neural Network Exchange https://onnx.ai/
Intermediate Python Docs
K-Means Clustering in Python
In Depth: k-Means Clustering
K-means Clustering in Python
Clustering With K-Means in Python
Unsupervised Machine Learning: Flat Clustering K-Means clusternig example with Python and Scikit-learn
ST at CES 2018 — Deep Learning on STM32


Configuring Marlin 1.1