• ## List of Physics Relativity Formulas Latex Code(First Part)

rockingdingo 2023-01-24 #physics #relativity #einstein

In 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.

• ## List of Linear Matrix Algebra Formulas Latex Code

math_beginner 2023-01-16 #math #matrix #algebra #eigenvalues #determinants

In 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.

• ## List of Physics Formulas Latex Code: Electricity & Magnetism (Graduate Level Physics)

physics_master 2022-12-24 #physics #electricity #magnetism

In 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.

• ## DeepNLP Equation Workspace: Manage, Edit, Share and Display Your Equations.

rockingdingo 2022-12-05 #equation editor #share #edit #display

In 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.

• ## List of Calculus Formulas Latex Code (Second Part: Integration)

rockingdingo 2022-12-04 #Math #calculus #integration

In 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.

• ## List of Calculus Formulas Latex Code (First Part: Limits, Differentiation)

rockingdingo 2022-12-04 #calculus #differentiation #Math

In 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).

• ## Introduction to On Device Recommendation (Edge Recommendation)

rockingdingo 2022-12-02 #on device #edgerec #Taobao #Alipay #Meituan #Kuaishou

In this blog, we will give you a brief introduction of most recent progress in On-Device Recommendation (Edge Recommendation) in real-world 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, on-device recommendations have enpowered many Mobile Apps to better respond to users' most real-time behaviors on mobile deivces, including clicks, scroll-donwns, likes, and many others. We will introduce three applications, including EdgeRec in Taobao, searchbar background words reranking in Alipay, search result reranking in Meituan-Dianping, short-video recommendation in KuaiShou, TfLite Implementation of Tensorflow, etc.

• ## List of Statistics Equations Latex Code

rockingdingo 2022-11-06 #Binomial #Poisson #Normal Gaussian #Covariance #Chi-Square

In 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 graduate-level statistics equations, e.g. Binomial, Poisson, Normal Distribution, Chi-Square Test, etc.

• ## Knowledge Graph Link Prediction Equations And Latex Code

rockingdingo 2022-10-23 #Knowledge Graph #Link Prediction #KG

In 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 low-dimensional embeddings of entities(E) and relations (R), and infer the missing part of [(?, r, t), (h, ?, t), (h, r, ?)].

• ## Few-Shot Learning And Zero-Shot Learning Equations Latex Code

rockingdingo 2022-10-23 #Few-Shot #Zero-Shot #MAML #ProtoNets #Bregman Divergences

In this blog, we will summarize the latex code of most fundamental equations of Few-Shot Learning and Zero-Shot Learning. Few-Shot Learning learns from a few-labelled examples and better generalize to unseen examples. Typical works includes Prototypical Networks, Model-Agnostic Meta-Learning (MAML), etc.

• ## Cheatsheet of Latex Code for Most Popular Machine Learning Equations

rockingdingo 2022-09-18 #GAN #VAE #KL-Divergence #Wasserstein #Mahalanobis

In 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 KL-Divergence, JS-Divergence, 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.

• ## Latex Code for Diffusion Models Equations

rockingdingo 2022-09-18 #Diffusion #VAE #GAN #Generative Models

In this blog, we will summarize the latex code of equations for Diffusion Models, which are among the top-performing generative models, including GAN, VAE and flow-based 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/2021-07-11-diffusion-models/

• ## Cheatsheet of Latex Code for Reinforcement Learning Equations

rockingdingo 2022-07-18 #rl #reinforcement learning

In 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.

• ## Cheatsheet of Latex Code for Financial Engineering and Quantitative equations

rockingdingo 2022-07-18 #financial engineering #black-sholes

In this blog, we will summarize the latex code of most popular equations for financial engineering. We will cover important topics, including Black-Scholes formula, Value at Risk(VaR), etc.

• ## Cheatsheet of Latex Code for Graph Neural Network(GNN) Equations

rockingdingo 2022-07-17 #graph neural network #gnn #gcn #gat #graphsage

In 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.

• ## Cheatsheet of Latex Code for Transfer Learning Equations

In this blog, we will summarize the latex code of most fundamental equations of transfer learning(TL). Different from multi-task 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 sub-space alignment, etc. In this post, we will dicuss more details of TL equations, including many sub-areas like domain adaptation, H-divergence, Domain-Adversarial Neural Networks(DANN), which are useful as quick reference for your research.

• ## Cheatsheet of Latex Code for Kernel Methods and Gaussian Process

rockingdingo 2022-07-11 #kernel #svm #gaussian process #gp #deep kernel learning

In 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).

• ## Cheatsheet of Latex Code for Multi-Task Learning Equations

rockingdingo 2022-07-11 #mtl #multi-task learning #mmoe #ple

In this blog, we will summarize the latex code of most fundamental equations of multi-task learning(MTL) and transfer learning(TL). Multi-Task Learning aims to optimize N related tasks simultaneously and achieve the overall trade-off between multiple tasks. Typical network structure include shared-bottom models, Cross-Stitch Network, Multi-Gate Mixture of Experts (MMoE), Progressive Layered Extraction (PLE), Entire Space Multi-Task Model (ESSM) models and etc. Different from multi-task learning. In the following sections, we will dicuss more details of MTL equations, which is useful for your quick reference.

• ## Cheatsheet of Latex Code for Most Popular Causual Inference and Uplift modelling Equations

rockingdingo 2022-07-10 #causal inference #uplift modelling #auuc #qini

Cheatsheet of Latex Code for Most Popular Causual Inference and Uplift modelling Equations

• ## Cheatsheet of Latex Code for Most Popular Recommendation and Advertisement Ranking module Equations

Ranking is a crucial part of modern commercial recommendation and advertisement system. It aims to solve the problem of accurate click-through rate(CTR) prediction. In this article, we will provides some of most popular ranking equations of commercial recommendation or ads system.

• ## 20 Tricks to Tell if Rolex Watch is Real or Fake

fashion_watch 2022-06-05 #ROLEX #FASHION #AIR-KING #GMT-MASTER-II #YACHT-MASTER-II #SUBMARINER #DAY-DATE

20 Tricks to Tell if Rolex Watch is Real or Fake

• ## Cheatsheet of Latex Code for Most Popular Natural Language Processing Equations

rockingdingo 2022-05-03 #nlp #latex #bert

Cheatsheet of Latex Code for Most Popular Natural Language Processing Equations

• ## Cross-Domain Recommendation in Commercial Recommendation System With application of MMD and Wasserstein distance

rockingdingo 2021-07-25 #cross domain recommendation #mmd #wasserstein

Cross-Domain Recommendation in Commercial Recommendation System With application of MMD and Wasserstein distance

• ## Deep Candidate Generation (DeepMatch) Algorithm in recommendation

rockingdingo 2021-07-25 #deep candidate generation #deepmatch #recommendation #vector retrieval

In 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.