What You’ll Learn Extend your machine learning models using simple techniques to create compelling and interactive web dashboards Leverage the Flask web framework for rapid prototyping of your Python models and ideas Create dynamic content powered b
# DDC-transfer-learning A simple implementation of Deep Domain Confusion: Maximizing for Domain Invariance which is inspired by [transferlearning][https://github.com/jindongwang/transferlearning]. The project contains *Pytorch* code for fine-tuning
TensorFlow.js, Google 提供的基于TensorFlow的Javascr ipt库。方便使用JS的开发者使用，并且可以为未来的边缘计算提供支持。TensorFlow. js: Machine Learning for the Web and beyond
acceleration, notably TensorFire(Kwok et al., 2017), Propel Layers APl, which provides higher-level model buildin
通过深度学习增强的视网膜光学相干断层扫描图像论文，pdf格式Research Article
VoL 9, No 12 1 Dec 2018 BIOMEDICAL OPTICS EXPRESS 6207
Biomedical Optics EXPRESS
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To achieve this we train a generator network as a feed-forward convolutional neural network
(CNN) GeG par
Batched Sparse Matrix Multiplication for Accelerating Graph Convolutional Networks
对图卷积网络进行加速的批量稀疏矩阵乘法
作者的ppt的pdf版本Formulation of Graph Convolution
GraphConvolution( A, X w, bias)
Feature
forb← o to batchsize
do for ch←0 to channel
y:=2a a,w
Y=AXW
do
具体描述Glow编译器的基础知识，glow是通过减少计算图的计算量来优化的have implemented a high-level intermediate represen
Variable
name: save saveLl
tation that allows a compiler to reason about and
Value: 0.000000e+C0 output: floaK
optimize high-level constructs such as tensors and
概述
在使用keras中的keras.backend.batch_dot和tf.matmul实现功能其实是一样的智能矩阵乘法，比如A,B,C,D,E,F,G,H，I,J,K,L都是二维矩阵，中间点表示矩阵乘法，AG 表示矩阵A 和G 矩阵乘法（A 的列维度等于G 行维度），WX=Z
import keras.backend as K
import tensorflow as tf
import numpy as np
w = K.variable(np.random.randint(10,si
火焰：铰接式表现力3D头部模型（TF）
这是基于Tensorflow的官方存储库。
我们还提供 （一个基于的）和代码，以 。
FLAME是一种轻量级且表现力强的通用头部模型，可从33,000多次精确对齐的3D扫描中获悉。 FLAME将线性标识形状空间（受3800名受试者的头部扫描训练）与铰接的脖子，下颌和眼球，姿势相关的校正混合形状以及其他全局表达混合形状相结合。 有关详细信息，请参见
Learning a model of facial shape and expression from
