
Latex for Financial Engineering Mathematics Formula MonteCarlo Simulations and Interest Rate Models
rockingdingo 20230521 #financial engineering #mathematics #financeIn this blog, we will summarize the latex code of most popular formulas and equations for Financial Engineering Formula and Equation MonteCarlo Simulations and Interest Rate Models. We will cover important topics including MonteCarlo Simulations, Bonds and Interest Rates, BlackDermanToy (BDT) model and CoxIngersollRoss (CIR) model.
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Latex for Financial Engineering Mathematics Formula (Forwards Puts and Calls)
rockingdingo 20230514 #Financial EngineeringIn this blog, we will summarize the latex code of most popular formulas and equations for Financial Engineering Formula and Equation part IForwards, Puts, and Calls. We will cover important topics including Forwards, PutCall Parity, Calls and Puts with Different Strikes, Calls and Puts Arbitrage, Call and Put Price Bounds, Varying Times to Expiration, Early Exercise for American Options, etc.
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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.
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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.
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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.
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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.
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