一个比特币交易机器人基于 Tensorflow LSTM 模型，仅供娱乐。 A Bitcoin trade robot based on Tensorflow LSTM model.Just for fun.
Single Image Random Dot Stereograms – SIRDS is a means to present 3D data in a 2D image. It allows for scientific data display of a waterfall type plot with no hidden lines due to perspective.
TensorFlow World – Concise and ready-to-use TensorFlow tutorials with detailed documentation are provided.
Effective Tensorflow – Tensorflow howtos and best practices. Covers the basics as well as advanced topics.
AlexNet3D – An implementations of AlexNet3D. Simple AlexNet model but with 3D convolutional layers (conv3d).
VGAN Tensorflow – Tensorflow implementation for MIT “Generating Videos with Scene Dynamics” by Vondrick et al.
3D Convolutional Neural Networks in TensorFlow – Implementation of “3D Convolutional Neural Networks for Speaker Verification application” in TensorFlow by Torfi et al.
Lip Reading – Cross Audio-Visual Recognition using 3D Architectures in TensorFlow – TensorFlow Implementation of “Cross Audio-Visual Recognition in the Wild Using Deep Learning” by Torfi et al.
android-yolo – Real-time object detection on Android using the YOLO network, powered by TensorFlow.
TensorFlowOnSpark – initiative from Yahoo! to enable distributed TensorFlow with Apache Spark.
NNFlow – Simple framework allowing to read-in ROOT NTuples by converting them to a Numpy array and then use them in Google Tensorflow.
Sonnet – Sonnet is DeepMind’s library built on top of TensorFlow for building complex neural networks.
tensorpack – Neural Network Toolbox on TensorFlow focusing on training speed and on large datasets.
TFRecords Guide semantic segmentation and handling the TFRecord file format.
TensorFlow Android Guide – Android TensorFlow Machine Learning Example.
TensorFlow Optimizations on Modern Intel® Architecture – Introduces TensorFlow optimizations on Intel® Xeon® and Intel® Xeon Phi™ processor-based platforms based on an Intel/Google collaboration.
Machine Learning with TensorFlow by Nishant Shukla, computer vision researcher at UCLA and author of Haskell Data Analysis Cookbook. This book makes the math-heavy topic of ML approachable and practicle to a newcomer.