67

So you can integrate with the rest of the code base. It seems your company uses a mix of Java and python. What are you going to do if a little corner of the site needs machine learning; pass the data around with a database, or a cache, drop to R, and so on? Why not just do it all in the same language? It's faster, cleaner, and easier to maintain. Know any ...


42

A CNN will learn to recognize patterns across space. So, as you say, a CNN will learn to recognize components of an image (e.g., lines, curves, etc.) and then learn to combine these components to recognize larger structures (e.g., faces, objects, etc.). You could say, in a very general way, that a RNN will similarly learn to recognize patterns across time....


41

Drew Conway published the Data Science Venn Diagram, with which I heartily agree: On the one hand, you should really read his post. On the other hand, I can offer my own experience: my subject matter expertise (which I like better as a term than "Substantive Expertise", because you should really also have "Substantive Expertise" in math/stats and hacking) ...


24

There may be a lot of reasons like: Workforce flexibility: One Java / Python programmers can be moved to other tasks or projects easily. Candidates availability: there are plenty of Java / Python programmers. You do not want to introduce a new programming language to later find out that there are no qualified workers or they are just too expensive. ...


23

Difference between CNN and RNN are as follows : CNN: CNN take a fixed size input and generate fixed-size outputs. CNN is a type of feed-forward artificial neural network - are variations of multilayer perceptrons which are designed to use minimal amounts of preprocessing. CNNs use connectivity pattern between its neurons is inspired by the organization of ...


18

Focus less on gaining skills and more on gaining experience. Try to actually solve some problems and post your work on github. You'll learn more in the process and be able to demonstrate knowledge and experience to employers, which is much more valuable than having a supposedly deep understanding of a topic or theory. Data Science is a pretty loaded field ...


15

Welcome to the site, Martin! That's a pretty broad question, so you're probably going to get a variety of answers. Here's my take. Data Science is an interdisciplinary field generally thought to combine classical statistics, machine learning, and computer science (again, this depends on who you ask, but other might include business intelligence here, and ...


15

This is an open area of research and it certainly depends on the way you frame the problem. If you're talking about multi-document summarization then the problem is slightly different than if you were talking about single-document summarization. It's worth briefly reviewing the literature. The link provided by u/Society Of Data Scientists is great and it'...


14

After reading your question, I became curious about the topic of time series clustering and dynamic time warping (DTW). So, I have performed a limited search and came up with basic understanding (for me) and the following set of IMHO relevant references (for you). I hope that you'll find this useful, but keep in mind that I have intentionally skipped ...


14

The Julia project is one which I actively contribute to, including the advanced computing and XGBoost libraries. So, I can definitely vouch for it's maintenence and the quality of the community. Some really good open source data science projects where even the beginners can contribute are: Sklearn: Always developing at a rapid pace, the sklearn community ...


14

The Google Research Blog should be helpful in the context of TensorFlow. In the above article, there is a reference to the Annotated English Gigaword dataset which is routinely used for text summarization. The 2014 paper by Sutskever et al titled Sequence to Sequence Learning with Neural Networks could be a meaningful start on your journey as it turns out ...


14

It is in general true that for purely data science and statistics exercises R offers the best and fastest (especially if using the data.table package) tools and methods, that otherwise would be heavier to implement in Python (I assume by Python we all mean Pandas, though). Most data scientists do in fact use R to perform their models and calculations, or ...


13

Let's work it out from the ground up. Classification (also known as categorization) is an example of supervised learning. In supervised learning you have: model - something that approximates internal structure in your data, enabling you to reason about it and make useful predictions (e.g. predict class of an object); normally model has parameters that you ...


12

Most common types of decision trees you encounter are not affected by any monotonic transformation. So, as long as you preserve orde, the decision trees are the same (obviously by the same tree here I understand the same decision structure, not the same values for each test in each node of the tree). The reason why it happens is because how usual impurity ...


11

I will try to answer your questions, but before I'd like to note that using term "large dataset" is misleading, as "large" is a relative concept. You have to provide more details. If you're dealing with bid data, then this fact will most likely affect selection of preferred tools, approaches and algorithms for your data analysis. I hope that the following ...


10

No, it means you are trying to find the inputs that make the output of the cost function the smallest. It doesn't mean that you should "minimize" use of it.


9

Business intelligence is perfect for you; you already have the business background. If you want to become a bona fide data scientist brush up on your computer science, linear algebra, and statistics. I consider these the bare essentials. I don't know about Scandinavia, but in the U.S., data science covers a broad spectrum of tasks ranging from full-time ...


9

Being good at a subject does not automatically make someone a good teacher, and it looks like Roman's answer on Quora has fallen into a trap of thinking everything he knows is simple and could be picked up quickly. Also making the potential student attempt things outside of a beginner level - effectively just by research on the web following a 2 paragraph ...


8

There are multiple ways to approach solving game playing problems. Some games can be solved by search algorithms for example. This works well for card and board games up to some level of complexity. For instance, IBM's Deep Blue was essentially a fast heuristic-driven search for optimal moves. However, probably the most generic machine learning algorithm ...


7

I do like Berkeley course on Data Science, will give a good foundation and taste for Data Science, After moved to udacity and coursera and many more resources. So if you have Programming skills than will need math and stat and a lot of visualization. Also will be great to get used to IPython because is essential to see every step(visualize)how it perform ...


7

I think there are a lot of important soft skills to consider in the Data Science domain. Here are some of them: Know for a fact what the goal is, spending a lot of time on data wrangling, models, visualization and reports when it was not all for the specific goal in mind is a waste. Communicating with less technical people is a skill in itself. Iterate ...


7

I've seen quite a few companies using the title Data Scientist for "Data Engineer" type roles. Particularly in the big data space. If the company is using Hadoop or a distributed framework like Spark to do it's analytics in then Java or Python (or probably Scala) would be the languages that would make the most sense .


7

It highly depends on the type of game and the information about the state of the game that is available to your AI. Some of the most famous game playing AIs from last few years are based on deep reinforcement learning (e.g. Playing Atari with Deep Reinforcement Learning), which is normal reinforcement learning (e.g. Q-learning) with a deep neural network as ...


6

First of all, the fact that you have known some Java, even ten years ago, already means that you don't "know nothing about programming" (I suggest you update the title of your question to reflect that - change "nothing" to "a little"). I'd like to make several points, which I hope will be useful to you. In terms of the level of programming proficiency, ...


6

There are plenty of them available. I do not know if I am allowed to do this (please let me know if it is wrong), but I develop one and it has already over 2 years on git hub (it actually started one years before github). The project is called rapaio, is on git hub here and recently I started to write a manual for it (some of my friends asked me about that)....


6

Just a few thoughts which aren't covered in the link I pasted above ... Big data != data science. If you are a data scientist you may or may not be using big data tools. Your question wasn't clear if you understand this or not, but the distinction is important. There are various careers that 'fit' in the data science spectrum. Instead of repeating them ...


6

The answer to your questions depend a lot on the nature of the data represented in the time series. You should ask yourself some questions to better understand what might or might not work, e.g.: Are the time sequences perfectly aligned? Are two slightly shifted time series considered similar or not? Are two time series with the same shape but different ...


5

First of all I should say you question probably is an off-topic and will be closed soon. Discussed at this SE site Anyway I can target you to similar questions discussed at this SE site already: Statistics + Computer Science = Data Science? Starting my career as Data Scientist, is Software Engineering experience required? Cross Validated SE A set of ...


5

Topic Modeling would be a very appropriate method for your problem. Topic Models are a form of unsupervised learning/discovery, where a specified (or discovered) number of topics are defined by a list of words that have a high probability of appearing together. In a separate step, you can label each topic using subject matter experts, but for your purposes ...


5

There is a very nice library of online machine learning algorithms from a group at NTU, called LIBOL. This would be a very good place to start experimenting with the algorithms. The accompanying user guide, and associated JMLR publication are very nice introductions to the basic algorithms in this field. Avrim Blum has an older and more technical survey ...


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