
List of Physics Relativity Formulas Latex Code(First Part)
rockingdingo 20230124 #physics #relativity #einsteinIn this blog, we will introduce most popuplar physics formulas in Relativity. We will also provide latex code of the equations. Topics include the Lorentz transformation, red and blue shift, general relativity, Riemannian Tensor and Einstein Field Equations, etc.
READ MORE 
List of Linear Matrix Algebra Formulas Latex Code
math_beginner 20230116 #math #matrix #algebra #eigenvalues #determinantsIn this blog, we will summarize the latex code for linear matrix algebra formulas, including matrix multiplication, transpose, inverse matrix, determinants, hermitian matrices, determinants, eigenvalues and eigenvectors, orthogonal matrices, etc.
READ MORE 
List of Physics Formulas Latex Code: Electricity & Magnetism (Graduate Level Physics)
physics_master 20221224 #physics #electricity #magnetismIn this blog, we will introduce most popuplar physics formulas in Electricity & Magnetism. This blog covers topics, including the Maxwell equations, force and potential, Gauge Transformations, Energy of the Electromagnetic Field, etc.
READ MORE 
DeepNLP Equation Workspace: Manage, Edit, Share and Display Your Equations.
rockingdingo 20221205 #equation editor #share #edit #displayIn this blog, we will introduce the DeepNLP Equation workspace, which helps users better manage, edit, share and display their equations. In the platform, users can manage their equations and latex code in a personal workspace, which enables users to create new equations (with latex code, personal tags), edit and save equations. It also creates a URL of your equation, which can be shared to the collaborators. In the following sections, we will give you the step by step instructions on how to create, edit and share an equation. Once you finish add the equation, you can copy the latex code of your equation to clipboard and paste the code to your preferred latex file system, e.g. Overleaf, etc.
READ MORE 
List of Calculus Formulas Latex Code (Second Part: Integration)
rockingdingo 20221204 #Math #calculus #integrationIn this blog, we will summarize the latex code for basic calculus formulas, including Limits, Differentiation and Integration. For integration formulas, we will cover the topic as standard form of integration, integration of 1/x, ln(x), Exponential e^{ax}, xe^{ax}, Integration by Parts, Differentiation of an Integral, Dirac Delta Function, etc.
READ MORE 
List of Calculus Formulas Latex Code (First Part: Limits, Differentiation)
rockingdingo 20221204 #calculus #differentiation #MathIn this blog, we will summarize the latex code for basic calculus formulas, including Limits, Differentiation and Integration. For limits formulas, we will cover sections including: L'Hospital Rule, Limits of Power, Limits of xln(x), Limits of x^{n}/n!. For differentiation formulas, we will cover Differentiation of Polynomial Function, Chain Rule for Differentiation, Differentiation of Multiplication, Differentiation of Division, Differentiation of Trigonometric formulas (sin, cos, tan, sec), and Differentiation of Hyperbolic formulas (sinh, cosh, tanh, sech, coth, cosech).
READ MORE 
Introduction to On Device Recommendation (Edge Recommendation)
rockingdingo 20221202 #on device #edgerec #Taobao #Alipay #Meituan #KuaishouIn this blog, we will give you a brief introduction of most recent progress in OnDevice Recommendation (Edge Recommendation) in realworld applications. Mobile AI systems and applications have been more popular due to increasing number of mobile devices and technology developments in deep learning based methods, e.g. model compression, distillation and so on. In recent years, ondevice recommendations have enpowered many Mobile Apps to better respond to users' most realtime behaviors on mobile deivces, including clicks, scrolldonwns, likes, and many others. We will introduce three applications, including EdgeRec in Taobao, searchbar background words reranking in Alipay, search result reranking in MeituanDianping, shortvideo recommendation in KuaiShou, TfLite Implementation of Tensorflow, etc.
READ MORE 
List of Statistics Equations Latex Code
rockingdingo 20221106 #Binomial #Poisson #Normal Gaussian #Covariance #ChiSquareIn this blog, we will summarize the latex code for statistics equations, ranging from the elementary statistics equations (e.g. mean and variance) to more advanced graduatelevel statistics equations, e.g. Binomial, Poisson, Normal Distribution, ChiSquare Test, etc.
READ MORE 
Knowledge Graph Link Prediction Equations And Latex Code
rockingdingo 20221023 #Knowledge Graph #Link Prediction #KGIn this blog, we will summarize the latex code of most fundamental and popular knowledge graph (KG) Equations, with special focus on the link prediction tasks. We will cover a wide range of models, including TransE, TransR, TransE, RotatE, SME(Linear), SimplE etc. Knowledge Graph consists of a set of triples [(h, r, t)]. h and t denotes the head node and tail node respectively. And r denotes multiple relation types. One common solution to the link prediction tasks is to learn lowdimensional embeddings of entities(E) and relations (R), and infer the missing part of [(?, r, t), (h, ?, t), (h, r, ?)].
READ MORE 
FewShot Learning And ZeroShot Learning Equations Latex Code
rockingdingo 20221023 #FewShot #ZeroShot #MAML #ProtoNets #Bregman DivergencesIn this blog, we will summarize the latex code of most fundamental equations of FewShot Learning and ZeroShot Learning. FewShot Learning learns from a fewlabelled examples and better generalize to unseen examples. Typical works includes Prototypical Networks, ModelAgnostic MetaLearning (MAML), etc.
READ MORE 
Cheatsheet of Latex Code for Most Popular Machine Learning Equations
rockingdingo 20220918 #GAN #VAE #KLDivergence #Wasserstein #MahalanobisIn this blog, we will summarize the latex code for most popular machine learning equations, including multiple distance measures, generative models, etc. There are various distance measurements of different data distribution, including KLDivergence, JSDivergence, Wasserstein Distance(Optimal Transport), Maximum Mean Discrepancy(MMD) and so on. We will provide the latex code for machine learning models in the following sections. We will also provide latex code of Generative Adversarial Networks(GAN), Variational AutoEncoder(VAE), Diffusion Models(DDPM) for generative models in the second section.
READ MORE 
Latex Code for Diffusion Models Equations
rockingdingo 20220918 #Diffusion #VAE #GAN #Generative ModelsIn this blog, we will summarize the latex code of equations for Diffusion Models, which are among the topperforming generative models, including GAN, VAE and flowbased models. The basic idea of diffusion models are to inject random noise to the feature vector in the forward process as markov chain models, and in the reverse process gradualy reconstruct the feature vector for generation. See below blogpost as reference for more details: Weng, Lilian. (Jul 2021). What are diffusion models? Lilâ??Log. lilianweng.github.io/posts/20210711diffusionmodels/
READ MORE 
Cheatsheet of Latex Code for Reinforcement Learning Equations
rockingdingo 20220718 #rl #reinforcement learningIn this blog, we will summarize the latex code of most fundamental equations of reinforcement learning (RL). This blog will cover many topics, including Bellman Equation, Markov Decision Process(MDP), Partial Observable Markov Decision Process(POMDP), DQN, A3C, etc.
READ MORE 
Cheatsheet of Latex Code for Financial Engineering and Quantitative equations
rockingdingo 20220718 #financial engineering #blacksholesIn this blog, we will summarize the latex code of most popular equations for financial engineering. We will cover important topics, including BlackScholes formula, Value at Risk(VaR), etc.
READ MORE 
Cheatsheet of Latex Code for Graph Neural Network(GNN) Equations
rockingdingo 20220717 #graph neural network #gnn #gcn #gat #graphsageIn this blog, we will summarize the latex code of equations of Graph Neural Network(GNN) models, which are useful as quick reference for your research. For common notation, we denote G=(V,E) as the graph. V as the set of nodes with size V=N, and E as the set of N_e edges as E = N_e. A is denoted as the adjacency matrix. For each node v, we use h_v and o_v as hidde state and output vector of each node.
READ MORE 
Cheatsheet of Latex Code for Transfer Learning Equations
rockingdingo 20220717 #machine learning #transfer learning #domain adaptation #DomainAdversarial Neural NetworksIn this blog, we will summarize the latex code of most fundamental equations of transfer learning(TL). Different from multitask learning, transfer learning models aims to achieve the best performance on target domain (minimized target domain test errors), not the performance of source domain. Typical transfer learning methods including domain adaptation(DA), feature subspace alignment, etc. In this post, we will dicuss more details of TL equations, including many subareas like domain adaptation, Hdivergence, DomainAdversarial Neural Networks(DANN), which are useful as quick reference for your research.
READ MORE 
Cheatsheet of Latex Code for Kernel Methods and Gaussian Process
rockingdingo 20220711 #kernel #svm #gaussian process #gp #deep kernel learningIn this blog, we will summarize the latex code of most popular kernel methods and Gaussian Process models, including Support Vector Machine (SVM), Gaussian Process (GP) and Deep Kernel Learning(DKL).
READ MORE 
Cheatsheet of Latex Code for MultiTask Learning Equations
rockingdingo 20220711 #mtl #multitask learning #mmoe #pleIn this blog, we will summarize the latex code of most fundamental equations of multitask learning(MTL) and transfer learning(TL). MultiTask Learning aims to optimize N related tasks simultaneously and achieve the overall tradeoff between multiple tasks. Typical network structure include sharedbottom models, CrossStitch Network, MultiGate Mixture of Experts (MMoE), Progressive Layered Extraction (PLE), Entire Space MultiTask Model (ESSM) models and etc. Different from multitask learning. In the following sections, we will dicuss more details of MTL equations, which is useful for your quick reference.
READ MORE 
Cheatsheet of Latex Code for Most Popular Causual Inference and Uplift modelling Equations
rockingdingo 20220710 #causal inference #uplift modelling #auuc #qiniCheatsheet of Latex Code for Most Popular Causual Inference and Uplift modelling Equations
READ MORE 
Cheatsheet of Latex Code for Most Popular Recommendation and Advertisement Ranking module Equations
rockingdingo 20220620 #recommendation #advertisement #ranking #sequential modellingRanking is a crucial part of modern commercial recommendation and advertisement system. It aims to solve the problem of accurate clickthrough rate(CTR) prediction. In this article, we will provides some of most popular ranking equations of commercial recommendation or ads system.
READ MORE 
20 Tricks to Tell if Rolex Watch is Real or Fake
fashion_watch 20220605 #ROLEX #FASHION #AIRKING #GMTMASTERII #YACHTMASTERII #SUBMARINER #DAYDATE20 Tricks to Tell if Rolex Watch is Real or Fake
READ MORE 
Cheatsheet of Latex Code for Most Popular Natural Language Processing Equations
rockingdingo 20220503 #nlp #latex #bertCheatsheet of Latex Code for Most Popular Natural Language Processing Equations
READ MORE 
CrossDomain Recommendation in Commercial Recommendation System With application of MMD and Wasserstein distance
rockingdingo 20210725 #cross domain recommendation #mmd #wassersteinCrossDomain Recommendation in Commercial Recommendation System With application of MMD and Wasserstein distance
READ MORE 
Deep Candidate Generation (DeepMatch) Algorithm in recommendation
rockingdingo 20210725 #deep candidate generation #deepmatch #recommendation #vector retrievalIn this post, we will talk about some realworld applications of deep candidates generation (vectorretrieval) 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.
READ MORE 
Tensorflow并行：多核(multicore)，多线程(multithread)
rockingdingo 20191001 #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。
READ MORE 
Tensorflow C++ API调用预训练模型和生产环境编译
rockingdingo 20181101 #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。
READ MORE