Baidu
Baidu has open-sourced its in-depth learning platform, Paddle, which has triggered a lot of interest from developers in the field of artificial intelligence, including some developers who have been practicing on Tensorflow and Caffe before. The Baidu open source Paddle, as a heterogeneous distributed deep learning system based on Spark, uses the GPU and FPGA heterogeneous computing to improve the data processing capacity of each machine, and temporarily obtained the industry's "simple, clean, stable, and speed. Faster, smaller memory footprint.†The evaluation has important implications for improving the data processing capabilities of each machine by using GPU and FPGA heterogeneous computing. However, the specific performance, but also need to wait for a few days to observe everyone's use of feelings.
The Google team announced that it has open-sourced the latest TF-slim database for TensorFlow, a lightweight package that can define, train, and evaluate models, as well as several major competitive networks in the image classification field. Test and define the model.
To further advance this area, the Google team today announced the open source Inception-ResNet-v2 (a convolutional neural network - CNN) that achieved the best results in the ILSVRC image classification benchmark. Inception-ResNet-v2 is an evolution of the earlier Inception V3 model and has gained some inspiration from Microsoft's ResNet paper.
Artificial Intelligence October Pregnancy Notes: 
On August 31, 1955, the term "artificial intelligence" first appeared on a seminar proposal that lasted two months and only 10 people participated. Proposers include John McCarthy (Dartmouth College), Marvin Minsky (Harvard University), Nathaniel Rochester (IBM), and Claude Shannon (Bell Telephone Laboratories) among others. The Dartmouth Summer Symposium (1956) later this year was widely considered to be the day of artificial intelligence research.
Stanford's first AI100 report: 
Stanford recently launched a project entitled "100 Years of Artificial Intelligence Research" and formulated specific plans. Spirited Jubilee of the past, attractiveness of artificial intelligence (AI) is largely from its commitment to achieve the vision. But in the last 15 years, most of its promises have already been realized. Artificial intelligence technology has long been in our lives. As they are transforming into the core strengths of society, research in the AI ​​field has changed from simply establishing intelligent systems to establishing human-recognized, trustworthy intelligent systems.
From world champion to data scientist:
The Kontron public class was invited to Pan Zhi, the chief scientist of iPIN. He received his PhD from Sun Yat-sen University at the end of 2004. He was in Hong Kong University of Science and Technology from February 2005 to August 2007, and from August 2007 to 2009. In the United States Hewlett-Packard Laboratories, conducted data mining, artificial intelligence and other aspects of research. In October 2009, he passed the 100-person plan to enter Sun Yat-sen University and worked in the Department of Computer Science. Since 2014, he has been the chief scientist of iPIN. This open class questions the impact of his core research direction, the experience of teaching at CUHK on iPIN's chief scientist, and how to filter out a vast array of data that really helps us. How can we analyze and share these data to make decisions about our own interests?
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