Hello there,

Since I have no previous experience in machine learning (only in software development), I was wondering if the following approach could be feasible at all or if I have to dig much deeper:

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I am currently developing a software interface (Python, running on a PC) to access sensor data that is being collected by a microcontroller and sent to the PC via USB.

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The sensors are arranged in a 64×64 matrix and are placed under a piece of foil (calling the combination of it “sensor foil”). They measure pressure created by human physical activity (e.g. a hand touching the sensor foil).

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What I want to achieve is to detect different body parts or objects placed on the foil. For example left/right hand placed flat on it, a glass being placed on it or an elbow.

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Is there an approach so high level that allows me to:

– Create a fixed set of events to be detected by the system: Left/right hand, elbow and glass placed on the sensor foil

– Feed the live raw sensor data to a machine learning system running on the PC

– Train the system with the events described above. This means performing the physical action like putting my left hand down and tell the system which of the previously defined events I just made occur

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After training is finished provide the system with the live sensor data, execute one of the defined physical actions and have the system recognize the according event along with a confidence level provided

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Is that something one of the available machine learning systems can do, considering this is a proof-of-concept project with one man behind it?

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My apologies, if this request is being formulated too broad. I will gladly take in any recommendations on reading up on the matter.

Thank you!

Dataset: Hotel Booking Demand Dataset

The primary commodity (the hotel room) is perishable product within 24hrs and for everyday in the calendar it has a very different lead-time with its corresponding room rates.

Eg: For Room A on 25th August 2019 every hour before the booking time on 25th Aug is a lead time, which can have different pricing. (Const pricing vs Dynamic pricing)

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Also the pricing of the room is dependent on the number of rooms left on the booking date, pricing of competitors in the area, events in the area, seasonality, day of the week, month of the year, inbound flight and train patterns of the region, meteorological information of the region etc.

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I want to know if there are people who have already worked on a problem solution, similar to this in the hotel industry? If yes what are the techniques of Data framing that you used and the models that you have tried this on?

Currently, I found a few solutions to convert models to onnx and keras to tflite, however, nothing is related to pytoch to tflite.

I’m implementing https://arxiv.org/pdf/1906.05909.pdf in this repo (https://github.com/MerHS/SASA-pytorch), but current implementation consumes too much GPU memory. (currently x0.5 less params than ResNet-50, x10 more mem consumption)

I think some sort of `matmul` or `view` are causing this problem, hence I’m working on changing matmul to einsum. (also I am not sure that I implemented it correctly)

Could anyone guess how the authors optimized this network?

Dear all,

I have a curiosity about GANs and what we can see around on article about this argument. In lots of article at example faces image, you are not able to understand if the image is real or is a fake generated by a GAN. My question is, this condition happen always or the fact the output face is impossible to distinguish if true or false to a real face happen only sometimes ? Hope to have been clear in what I am intend and ask.

Thanks,

Fabio

Hi Reddit,

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I am to work on a Speech Recognition project for the next few weeks/months or so. I don’t have any prior knowledge on the subject, but I roughly guess a basic architecture should not be far from an encoder – decoder architecture. I have to gain insights on the field and put a model in production by the end of the year.

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For now, I just want to be able to transcript audio data into text. I have first to understand the basics of audio data. I guess I will have to read some papers about Fourier transforms, spectrograms, denoising, filtering and so on.

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I have a few questions for you though.

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– First, do you have good resources (MOOC, courses, …) to learn Speech Recognition ? I tried to look for some, and I found a Stanford course (http://web.stanford.edu/class/cs224s/syllabus.html) from 2017. Given the syllabus, would you say it is a good resource to learn from ?

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– Then, is it worth it to implement my own model from scratch, or should I use a pre-existing library ? The audio data I want to train my model on are very task-dependent, and I don’t know if a pre-trained model would be good enough to recognize specific terms. On the other hand, I won’t have as much data or computational power as Google to train my own model. Given these elements, what library would you recommend ? I think the ideal solution would be to use a pre-trained model and fine-tune it on my data. Of course, any relevant resources would be much appreciated 🙂

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– Overall, what strategy would you recommend me to follow ? I don’t know where to look and where to start.

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Thank you so much !

Turing wrote a paper titled “Intelligent Machinery” in 1948. This is a highly original work, introducing ideas such as genetic algorithms and neural networks (what he called “unorganized machines”) with learning capabilities, and reinforcement learning. I believe “Intelligent Machinery” is the most detailed treatment of A.I. written before 1950. It was not published during Turing’s lifetime [*].

Rather than giving a detailed summary, I will just quote Turing’s own abstract:

Abstract The possible ways in which machinery might be made to show intelligent behaviour are discussed. The analogy with the human brain is used as a guiding principle. It is pointed out that the potentialities of the human intelligence can only be realised if suitable education is provided. The investigation mainly centres round an analogous teaching process applied to machines. The idea of an unorganised machine is defined, and it is suggested that the infant human cortex is of this nature. Simple examples of such machines are given, and their education by means of rewards and punishments is discussed. In one case the education process is carried through until the organisation is similar to that of an ACE.

Link to the paper: https://weightagnostic.github.io/papers/turing1948.pdf

h/t hackernews

Abstract Although probabilistic programming is widely used for some restricted classes of statistical models, existing systems lack the flexibility and efficiency needed for practical use with more challenging models arising in fields like computer vision and robotics. This paper introduces Gen, a general-purpose probabilistic programming system that achieves modeling flexibility and inference efficiency via several novel language constructs: (i) the generative function interface for encapsulating probabilistic models; (ii) interoperable modeling languages that strike different flexibility/efficiency trade-offs; (iii) combinators that exploit common patterns of conditional independence; and (iv) an inference library that empowers users to implement efficient inference algorithms at a high level of abstraction. We show that Gen outperforms state-of-the-art probabilistic programming systems, sometimes by multiple orders of magnitude, on diverse problems including object tracking, estimating 3D body pose from a depth image, and inferring the structure of a time series.

Project Page https://probcomp.github.io/Gen/

Paper https://dl.acm.org/citation.cfm?id=3314221.3314642

Code https://github.com/probcomp/Gen

Article on MIT news about this work. The article is a bit too hyped, but just including here for completeness, as the work looks solid on its own without this article.