Hello,

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I have monitoring system watching for bandwidth, connections and connections rates from multiple firewalls, which is stream of counters with interval 5 min.

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My current system create baseline from data for last 4 weeks and compare current value with baseline. It is ok but it either give me lots of false alerts or too slow to react without additional triggers. Is there anything better available today ? Some system I can feed data in that will learn patterns and identify outages in real time.

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Open source, but that I can use without getting into machine learning theory too deep just to start using it 🙂

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Thank you

We ran an event in NYC, Tel Aviv, and SF, where we asked AI companies to speak about what they do for 10-15 minutes. Seems I can’t link all of them because of some reddit limitation, so here are a few links but this is the YouTube playlist with all the talks.

• Jason Yosinski from Uber AI Labs on analyzing parameter updates in training
• Martin Aguinis – an overview of AI at Google
• K Health – personalized health
• Hopper – predicting when to buy flight tickets
• Giphy – contextually, culturally, relevant gifs
• Nexar – Auto safety
• NEC – Computer vision for skin cancer
• Taranis – Drones scanning farms for pests
• RigD – Crisis Management
• D-ID – Anti facial recognition tech
• Pavan Kumar, CTO of Cocoon Health

I would like to make a deep-learning based voice assistant for an application I have that controls a digital camera. Some example commands are “auto focus”, “set zoom to 2”, “turn off flash”, etc.

I see two ways of going about this:

1. Train a model that classifies an audio snippet as containing one of the commands or background noise. This seems easier than option 2 but also less robust, as I would have to retrain the model every time I add a new command. Also not sure how numbers would work (record myself saying every number up to like 100?).

2. Use STT to convert audio to text and do some fuzzy string matching to see if it matches a command. I’ve downloaded Mozilla’s DeepSpeech and it did not seem to work very well, so I’m guessing that creating a good STT model is very difficult.

Which of these is a better approach? Or is there some in-between approach that’s even better?

From the project page:

Safety Gym

We’re releasing Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training. We also provide a standardized method of comparing algorithms and how well they avoid costly mistakes while learning. If deep reinforcement learning is applied to the real world, whether in robotics or internet-based tasks, it will be important to have algorithms that are safe even while learning—like a self-driving car that can learn to avoid accidents without actually having to experience them.

https://openai.com/blog/safety-gym/

I’ve had success with sklearn in production, as well as with TF.

Is statsmodels something that can run in production? Its documentation doesn’t seem up to par but its code base doesn’t look bad.

Hi everyone, I’m currently trying to develop an LSTM RNN for predicting train delays. I looked into Time Series Forecasting Models and different approaches but can’t seem to figure out which model to use.

I want to implement multiple features, like delay, train number, date, time and current weekday. The output of the model should be a delay in minutes.

I have trouble understanding the difference between Multistep, Multivariate and Multistep-Multivariate Time Series Forecasting.

Can somebody please elaborate?

I want to implement offline handwriting ocr within an application. It should work on photographs of handwritings (=no scanned images). I tried tesseract and Google Cloud Vision. Both seem not to work well with handwritings.

Are there any handwriting specialized APIs/libraries with high accuracy? I would like to use something finished – the focus of the project should not be on building/training a model. Whats the state of the art in that specific area?

Ive searched around a little bit but couldnt find anything suitable.

Thankful for every hint I get.

I have a dataset of electrical outages and it is extremely imbalanced, <2% of all of the data are positive classes. I am using weather station data to try to predict the probability of an outage occurring near the weather stations.

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When I try any other model I have to rebalance the data to get any good results. However I have recently tried hierarchical Bayesian logistic regression and it performs just fine without resampling. In my methodology every individual weather station has a unique intercept and coefficients, but they are each drawn from a parent distribution.

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What I would like to discuss is why does the hierarchical approach perform so much better on the imbalanced dataset?

Hi! So I’m building a classifier which primarily looks at text, but I also want to include other features, which are non-text, and I was wondering what is the best way to do it? I feel like just adding another dimension in the vector which represents the text might cause these features to get ‘lost’, but maybe that’s not true. Is ther there some sort of agreed upon way of including these additional non-text features in? By non-text I mean just information which is not part of the body of the text, like some other meta data.

Thanks!