Hello,

I’m working on a project from http://www2.informatik.uni-freiburg.de/~cziegler/BX/ and I’m given a question “If I like Lord of the Rings, what else I should read?”. How would you proceed? My first thought was find other users/readers who like the book too and find what else they rated high. So, I’ve tried to create a pivot table, which ran out of memory (there’s over 1.3M rows), so I’ve create a group dataframe instead but I’m kinda in how to make a correlation. My second thought was to create clusters, find in which cluster is LOTR and recommend other books from the cluster. But I’m not sure about the later one.

Any insight would be most appreciated. Thanks!

For those struggling to understand AD and dual numbers, this article might help :)!

If I understand correctly, AD essentially computes an derivative of each operator using — for example, dual numbers — analytically exact methods, and combine them using the chain rule. This is feasible because programmable functions are defined using “primitive” operators, and we can compute their derivatives algorithmically (or analytically).

edit:

noticed the author has another multivariate example.

edit 2:

This was also helpful.

Hello everyone.

Recently, I’ve been thinking about starting a blog on Deep Learning, but when I look around, there’s just so many that are doing such a good work! There are some awesome blogs on Medium that go through ML topics both for knowledgeable and expert readers, some intriguing blog on a number of platforms that publish articles on the latest big things, so to speask, in ML, and vlogs on YouTube that are clearly spending a lot of time and resources on their videos! So, I wanted to ask you guys this. What do you think is missing in blogs? If you wanted to make a blog on ML what would you write about? I really want to somehow contribute back to this awesome community that has made it possible to learn just so much for free!

Thank you 🙂

Hello… may I question something?

I am a student studying at a foreign university lab.

I am studying stock price forecasting.

Adjusting the LSTM unit and layer is complete.

The topics(plan) of the next research were auto-keras or NLP.

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But the professor said that the topic was too broad.

he wants Step by step to build up my capabilities.

So I looked for all the paper related to stock predict.

Recent research was based on the attention mechanism or the process of using natural language like news.

The following research themes should be decided, but the gap between basic research and the latest research is too large.

I do not know what to make the next research topic.

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In the case of CNN, there are various networks such as LeNet, AlexNet, ZFNet, GoogLeNet, VGGNet, and ResNet

However, LSTM is SOTA(LSTM: A Search Space Odyssey https://arxiv.org/abs/1503.04069 ) in RNN

So, in the case of RNN is LSTM only

I think that methodology of improving accuracy of LSTM should be the subject of research.

do you have any course for forecasting time series data?

I do not know how to get directions

Your valuable opinions and thoughts will be very much appreciated.

Sorry if this is a repost, I posted it before but I think it got picked up by a spam filter, because I don’t see it on the front page at all (please correct me if this is not the case).

Here’s the link to the hackathon

https://azureai.devpost.com/

If you’re looking for a team you can post a profile here

https://azureai.devpost.com/participants?search%5Bonly_looking_for_teammates%5D=1

I’m currently trying to build a multivariate model to predict stock market movements using LSTM. The model is not seq-to-seq, but rather seq-to-one, if that matters.

I’ve read that walk-forward validation is the ‘gold-standard‘ for validation in time-series forecasting and that crossvalidation doesn’t work due to the spatial-temporal relevancy of the data.

This creates some weird implications for data normalization…

I’ve firmly held the belief that information leakage can spoil a model by providing unreasonable in-sample performance accuracy/loss. Consequently, I’m pretty careful when train-test splitting and then using custom tranforming pipelines to standardize the data (i.e. fit_transform() vs. transform() ). How do you overcome this issue? Is it really that big of a deal to split before standardization?

Main question: If you’re using a moving-window walk-forward validation, how would you handle train/test data splits and data normalization?

Several ebooks with overall good quality. Might be useful for some.

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Bundle

Hello everyone, I’m an engineering student and recently I was notified that my first paper was accepted in a regional conference of AI. I’m really happy about it and I want to share the preprint paper with you. Naturally, there is a lot of future work and improvements to do since it is my first research experience.

Detecting DNS Threats: A Deep Learning Model to Rule Them All

Abstract:

Domain Name Service is a central part of Internet regular operation. Such importance has made it a common target of different malicious behaviors such as the application of Domain Generation Algorithms (DGA) for command and control a group of infected computers or Tunneling techniques for bypassing system administrator restrictions. A common detection approach is based on training different models detecting DGA and Tunneling capable of performing a lexicographic discrimination of the domain names. However, since both DGA and Tunneling showed domain names with observable lexicographical differences with normal domains, it is reasonable to apply the same detection approach to both threats. In the present work, we propose a multi-class convolutional network (MC-CNN) capable of detecting both DNS threats. The resulting MC-CNN is able to detect correctly 99% of normal domains, 97% of DGA and 92% of Tunneling, with a False Positive Rate of 2.8%, 0.7% and 0.0015% respectively and the advantage of having 44% fewer trainable parameters than similar models applied to DNS threats detection.

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Thanks for reading, have a nice day!