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Cheatsheet of Latex Code for Most Popular Machine Learning Equations
rockingdingo 2021-08-22 #latex #machine learning #equations #nlp #recommendationCheatsheet of Latex Code for Most Popular Machine Learning Equations
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Cross-Domain Recommendation in Commercial Recommendation System With application of MMD and Wasserstein distance
rockingdingo 2021-07-25 #cross domain recommendation #mmd #wassersteinCross-Domain Recommendation in Commercial Recommendation System With application of MMD and Wasserstein distance
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Deep Candidate Generation (DeepMatch) Algorithm in recommendation
rockingdingo 2021-07-25 #deep candidate generation #deepmatch #recommendation #vector retrievalIn this post, we will talk about some real-world applications of deep candidates generation (vector-retrieval) models in the matching stage of recommendation scenario. Commercial recommendation system will recommend tens of millions of items to each user. And the recommendation process usually consists of two stages: The first stage is the candidate generation(matching) stage, a few hundred candidates are selected from the pool of all candidate items. The second stage is the ranking stage in which hundreds of items are ranked and sorted by the ranking score. Then the top rated items are displayed to users.
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Tensorflow并行：多核(multicore)，多线程(multi-thread)
rockingdingo 2019-10-01 #tensorflow #并行 #多核 #多线程 #parallelism #multicore利用tensorflow训练深度神经网络模型需要消耗很长时间，因为并行化计算就为提升运行速度提供了重要思路。Tensorflow提供了多种方法来使程序的并行运行，在使用这些方法时需要考虑的问题有：选取的计算设备是CPU还是GPU，每个CPU多少核的资源并行计算，构建图Graph时消耗资源如何分配等等问题。下面我们以Linux多核CPU的环境为例介绍几种常见方法来提升你的tensorflow程序的运行速度。
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Tensorflow C++ API调用预训练模型和生产环境编译
rockingdingo 2018-11-01 #tensorflow #cpp #c++ #build #nlp #deep learning研究如何打通tensorflow线下python脚本训练建模，线上生产环境用C++代码直接调用预先训练好的模型完成预测的工作，而不需要用自己写的Inference的函数。因为目前tensorflow提供的C++的API比较少，所以参考了几篇已有的日志，踩了不少坑一并记录下来。写了一个简单的ANN模型对Iris数据集分类的Demo。
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