A neural network that transforms a design mock-up into a static website.
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Updated
May 26, 2021 - HTML
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A neural network that transforms a design mock-up into a static website.
A High-Quality Real Time Upscaler for Anime Video
Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
An open source library for face detection in images. The face detection speed can reach 1000FPS.
Build your neural network easy and fast, 莫烦Python中文教学
Hide screen when boss is approaching.
机器人视觉 移动机器人 VS-SLAM ORB-SLAM2 深度学习目标检测 yolov3 行为检测 opencv PCL 机器学习 无人驾驶
深度学习入门教程, 优秀文章, Deep Learning Tutorial
A Deep-Learning-Based Chinese Speech Recognition System 基于深度学习的中文语音识别系统
Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
CNN-RNN中文文本分类,基于TensorFlow
Code examples for new APIs of iOS 10.
请问可以直接training tmfile出来吗? 因为tengine-convert-tool covert 会有error
tengine-lite library version: 1.4-dev
Get input tensor failed

或是有例子能training出下面tmfile 呢?
, Speaker Verification, Speech Synthesis, Text-to-Speech (TTS), Language Modelling, Singing Voice Synthesis (SVS), Voice Conversion (VC)
TensorFlow template application for deep learning
NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.
Collection of popular and reproducible image denoising works.
Tools to Design or Visualize Architecture of Neural Network
The deeplearning algorithms implemented by tensorflow
MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB
Deep Learning Based Free Mobile Real-Time Face Landmark Detector. Contact:jack-yu-business@foxmail.com
The current examples are in the form of scripts. To make easier and more interactive for users of the library it would help to have notebooks demonstrating these examples. For now the notebooks would go under examples folder under branch 2.0 where porting to Python 3+ is happening.
Datasets, tools, and benchmarks for representation learning of code.
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Hi I would like to propose a better implementation for 'test_indices':
We can remove the unneeded np.array casting:
Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))
Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))