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Bizdustry – Business & Economics

Bizdustry is a make money forum where members can talk about business, economics, finances, budgeting, investing and anything Crypto related. Members earn 0.01$ for every message they publish and 1$ for each member that is referred to the forum. We strive to provide the best quality content and…

education – MOOC recommendations for learning algorithms based on topics covered in Daspupta, Papadimitriou & Vazirani

I would appreciate some advise/mentoring on self-study of algorithms. Based on my several years of work as a software developer (non CS background), I have some good grip on standard data structures (linked lists, stacks etc.) and basic sorting and searching algorithm characteristics (time/space complexity).

My goal of learning is to take my knowledge to next level (learning graph theory and advanced algorithm techniques such as dynamic and linear programming) to be able to use it in a variety of situations incl. AI and computationally complex domain problems.

So I purchased 2 books — Algorithm Design Manual from Skiena and Algorithms by Dasgupta et al. I am aware of the usual advice of CLRS, but decided to skip it for now as my goal at this stage is not proofs.

Now my question is: even though I found the style of writing of Dasgupta book rather appealing, topic is often difficult for unguided, unsupervised learning.

Are there any recommendable online courses (free or otherwise) which would cover topics from Dasgupta book? In general, I found the topics covered in Stanford Algorithm specialization (coursera and edX) rather good, but any further options would be helpful!

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education – What topics should one include in a course Mathematics for Computer Science?

I am going to teach a second year undergraduate course Mathematics for Computer Science next year. And I have to choose topics myself.

The university where I am working is already offering a comprehensive set of CS and applied mathematics courses, such as: Algorithms, Probability, Statistics, Machine learning, mathematical modeling. Thus I would like to choose some topics which are not covered in these coureses. Some ideas I have include:

  • How to use SageMath
  • Basic combinatorics
  • Generating functions
  • Graph theory
  • Random graphs
  • SAT solvers

Are there any other mathematics topics which may be useful for CS students?

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microservices – Topics best practices – AWS SNS and others

I’m designing a medium size project that will have few decoupled microservices.

I want to avoid costly architecture mistakes at the very beginning.

What I’m wondering (I googled and didn’t found any answer) is if SNS topic should be relatively generic (e.g. “orders-events-topic”) or rather there should be separate topic per each event (e.g. “order-created-topic”, “order-accepted-topic”, etc.).

I realize that “it depends” – nevertheless I’d like to make a mindful decision.

machine learning – Tweet Classification into topics- What to do with data

Good evening,
First of all, I want to apologize if the title is misleading.
I have a dataset made of around 60000 tweets, their date and time as well as the username. I need to classify them into topics. I am working on topic modelling with LDA getting the right number of topics (I guess) thanks to this R package, which calculates the value of three metrics(“CaoJuan2009”, “Arun2010”, “Deveaud2014”). Since I am very new to this, I just thought about a few questions that might be obvious for some of you, but I can’t find online.

  1. I have removed, before cleaning the data (removing mentions, stopwords, weird characters, numbers etc), all duplicate instances (having all three columns in common), in order to avoid them influencing the results of topic modelling. Is this right?

  2. Should I, for the same reason mentioned before, remove also all retweets?

  3. Until now, I thought about classifing using the “per-document-per-topic” probability. If I get rid of so many instances, do I have to classify them based on the “per-word-per-topic” probability?

  4. Do I have to divide the dataset into testing and training? I thought that is a thing only in supervised training, since I cannot really use the testing dataset to measure quality of classification.

  5. Antoher goal would be to classify twitterers based the topic they most are passionate about. Do you have any idea about how to implement this?

Thank you all very much in advance.