人工智能资料库：第56辑（20170524）

作者：chen_h
微信号 & QQ：862251340
微信公众号：coderpai

---

1.【代码】paraphrase-id-tensorflow

简介：

Various models and code for paraphrase identification implemented in Tensorflow (1.1.0).

I took great care to document the code and explain what I’m doing at various steps throughout the models; hopefully it’ll be didactic example code for those looking to get started with Tensorflow!

So far, this repo has implemented:

• A basic Siamese LSTM baseline, loosely based on the model in Mueller, Jonas and Aditya Thyagarajan. “Siamese Recurrent Architectures for Learning Sentence Similarity.” AAAI (2016).

• A Siamese LSTM model with an added “matching layer”, as described in Liu, Yang et al. “Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention.” CoRR abs/1605.09090 (2016).

• The more-or-less state of the art Bilateral Multi-Perspective Matching model from Wang, Zhiguo et al. “Bilateral Multi-Perspective Matching for Natural Language Sentences.” CoRR abs/1702.03814 (2017).

PR’s to add more models / optimize or patch existing ones are more than welcome! The bulk of the model code resides in duplicate_questions/models

A lot of the data processing code is taken from / inspired by allenai/deep_qa, go check them out if you like how this project is structured!

原文链接：https://github.com/nelson-liu/paraphrase-id-tensorflow

---

2.【代码】Face classification and detection

简介：

Real-time face detection and emotion/gender classification using fer2013/IMDB datasets with a keras CNN model and openCV.
IMDB gender classification test accuracy: 96%.
fer2013 emotion classification test accuracy: 66%.

Emotion/gender classification of the B-IT-BOTS robotics team :)

原文链接：https://github.com/oarriaga/face_classification

---

3.【博客】TensorFlow Dev Summit 2017: Integrating Keras and TensorFlow

简介：

I am briefly sharing a video from the last TensorFlow Dev Summit in February 2017. My choice has fallen to a presentation by François Chollet of the deep learning library API Keras and its integration with TensorFlow. As Dr. Chollet explains, Keras integrated with TensorFlow promises to streamline deep learning frameworks in ways that will be increasingly user-friendly, rendering the mass adoption of these software developments a more feasible reality:

原文链接：https://theintelligenceofinformation.wordpress.com/2017/05/22/tensorflow-dev-summit-2017-integrating-keras-and-tensorflow/

---

4.【博客】Understanding and Implementing CycleGAN in TensorFlow

简介：

Transferring characteristics from one image to another is an exciting proposition. How cool would it be if you could take a photo and convert it into the style of Van Gogh or Picasso!

原文链接：https://hardikbansal.github.io/CycleGANBlog/?utm_campaign=Revue+newsletter&utm_medium=Newsletter&utm_source=revue

---

5.【资源】Deep Learning Resources

简介：

比较多样化的资源。

原文链接：http://www.jeremydjacksonphd.com/deep-learning-resources/

---