Hi, I’m Sumit Srivastava, founder of Projell.com . We made this after dealing with the data hell like low data availability, high data procuring cost, huge time sink for data collection, and privacy concerns over the user data.

This prompted me to build an easy way to generate synthetic data for machine learning models. This primarily uses GANs, but we use techniques which are most efficient for specific usecases.

Areas where we’ve found it useful are biomedical, drone imagery, satellite imagery, retail, and autonomous mobility.

As already prominent in the ImageNet challenge, the state of the art is using synthetic data to gain higher accuracy. [ https://paperswithcode.com/sota/image-classification-on-imagenet ]

Google, for their autonomous vehicles, used millions of miles of real driving data and billions of miles of synthetic data. It is clear where the world is moving towards.

I would be happy to share the tools with everyone since dealing with data is something we struggled with and don’t want anyone to struggle anymore. This is probably only the first step towards building something robust that can reduce with as much data hassles as possible, if not all.

I was trying to understand the skipgram model of word2vec, and I had some problems in understanding the details. I’m clear about the high level idea – given a word, predict the context of the word. However, when you actually train the model, what is the input and output of the model for a particular training instance? To be more concrete with an example, disregarding all sophisticated techniques like negative sampling etc., if I have the sentence “it is a beautiful day today”, the input to the cbow version would be average of one-hot encoding of “it”, “is”, “a”, “day”, “today” and the output should ideally be one-hot encoding of “beautiful”. For skip-gram, I’m confused – given input one-hot encoding of “beautiful”, what should be the output be? Should be average of one-hot encoding of “it”, “is”, “a”, “day”, “today” in a single training instance or “it”, “is”, “a”, “day”, “today” in 5 separate training instances? I tried to go through the gensim codebase to understand what they do, but it’s not clear.

As an extension to this question, I also wanted to know what happens in negative sampling. The way I have understood it is that instead of forcing determinate values in the output vector to say that we want each element to match precisely to the expected one-hot encoding of the output, we say that we want to enforce 1s and 0s at only a select few places in the vectors (corresponding to positive and negative samples), which reduces the amount of back-propagation. Is this correct?

Hello, so I am following this tutorial online to get started in Tensorflow since the Tensorflow documentation is not clear to new beginners.

One of the challenges that I see common is to train a model to differentiate between legitimate reviews and random reviews. Here is a CSV file:

https://raw.githubusercontent.com/dtsclife93/rawfiles/master/areviews.csv

In this CSV, there is a column for the written review and a column showing if it was a legitimate review (1 = legit review, 0 = not legitimate)

Whats the best way to train the model to be able to detect legit reviews from non-legit reviews and can I use this trained model to input my own review and Tensorflow outputs a 1 or 0.

Hello everyone, I’m a masters student in CS and I’m going to attend a workshop on deep learning this month. I hope asking this question here is fine.

This will be my first time attending a workshop so I’m not sure what to expect. I want to know from people who have been to such events before, what should I look for? There are scheduled talks from professors all over and I assume a lot of PhD and postdocs will attend but I don’t see myself really good at networking. I plan on joining a PhD program after my masters. Any help or guidance is really appreciated.

Thank you.

The title says most of it.

The two L2 constrained black box attacks I’ve found so far are Houdini and ZOO. From my understanding of adaptive these are not adaptive correct?

Assuming you have only a VERY basic understanding of math and stats, and little coding skills, would you read Geron’s book first or Andreas Mueller’s book first?

https://lexfridman.com/siraj-raval/

https://twitter.com/lexfridman/status/1133426787793293312

https://www.youtube.com/watch?v=-HwZR4zapqM&fbclid=IwAR2qORm1SM15VyFmGw30q1nTlfW01q5SUbLE5ask06dSBIdmUb22QDo2Ys8

I guess this was due to the info getting out of his scams. As far as I can tell, he has not made a statement on this.

Hey all

I’ve mostly seen hybrid recommender systems that mix Collaborative Filtering with content based methods.

However, is it not possible or viable to combine two collaborative filtering methods , such as Item-based CF using KNN, and Matrix Factorization? Isn’t this a hybrid approach as well?

I’m looking to combine their results and produce recommendations that are both novel and diverse, as part of a project. Any input would be great.

Suppose we have N events and the goal is to somehow group these N events in D different groups. Then each of those groups will be sent to an optimization algorithm and will return a value. The lower the value the better.

Assuming we do not have access to the optimization algorithm, how can I use machine learning to study how to group these N events in D different groups?

Example. For sake of simplicity, assume we have 100 events and I have two options:

• Group each of them individually and run the optimization process on each of them. I get an overall metric X
• Group all the events in a single group and run the optimization process. I get a metric Y.

The goal is to group these events such as we get the lowest error (the groups do not need to have the same number of events, it could be whatever).

Any clue?

At Hugging Face, we recently started distilling models starting with DistilBERT – a distilled version of BERT. We recently distilled the small version of GPT-2, which has the following parameters:

81,9M parameters vs 124M for GPT-2/small (66% parameters)

Weighs 336Mb vs 523Mb for GPT-2/small (64% disk size)

On CPU and GPU, the average forward pass of DistilGPT-2 is 51% that of GPT-2/small (twice as fast).

The absolute increase in perplexity on WikiText-103 is 3.5 points (15.0 -> 18.5).

We have added it to our app write with transformer, as well as our two repos transformers (along with a tutorial on how to distill transformers and example scripts!) and swift-coreml-transformers. We have successfully run it on an iPhone 7 and it is 38% faster than GPT-2 on an iPhone X with neural engine.