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Author: torontoai

[P] Beginner Training Model, Head Start?

Written on . Posted in Reddit MachineLearning.

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.

submitted by /u/CuriousKindo88
[link] [comments]

[D] What to expect form a ML/DL academic workshop?

Written on . Posted in Reddit MachineLearning.

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.

submitted by /u/constantdilema
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Top Experts from Government, Industry Join to Take On Critical AI Issues at GTC DC

Written on . Posted in NVIDIA.

Influential leaders and industry experts will give an inside look at AI policy matters at GTC DC, the largest AI conference in Washington, from Nov. 4-6.

Key topics to be focused on include the national AI strategy, cybersecurity, healthcare, workforce training and diversity.

Can’t-miss AI policy panels taking place at GTC DC include:

AI in America

U.S. CTO Michael Kratsios will kick off a series of panels on AI policy with a keynote addressing how the federal government is supporting American leadership in AI.

Kratsios headed the development of the executive order on AI and leads the White House’s Select Committee on Artificial Intelligence. He’ll share updates from the administration on how the order is being implemented.

The next panel will focus on national AI strategy. Experts involved with the executive order will delve into the details of how it’s being applied, and how private citizens can bring AI to their businesses.

The panel, moderated by David Luebke, vice president of research at NVIDIA, will share firsthand knowledge of the state of federal AI adoption and the investments being made in R&D, and discuss policies that are accelerating the implementation of AI in businesses and government agencies.

Panelists include:

  • Jason Matheny, founding director at Georgetown’s Center for Security and Emerging Technology and Commissioner in the National Security AI Commission
  • Lynne Parker, assistant director for AI at the White House Office of Science and Technology
  • Elham Tabassi, chief of staff of the IT Lab at the National Institute of Standards and Technology
  • Robert Atkinson, president at the Information Technology and Innovation Foundation

Hindering the Hackers: AI and Cybersecurity

As technology improves, so do cyberattacks and massive data breaches. But cybersecurity experts will take part in a panel on how AI can help.

Moderated by Iain Cunningham, vice president of intellectual property and cybersecurity at NVIDIA, the panel features leaders in data security who will pinpoint how AI can prevent cyberattacks and how AI policy can safeguard data.

Panelists include:

  • Moira Bergin, subcommittee director, cybersecurity, infrastructure protection for the House Committee on Homeland Security
  • Coleman Mehta, senior director of U.S. policy at Palo Alto Networks
  • Daniel Kroese, associate director of the national risk management center at the Cybersecurity and Infrastructure Security Agency
  • Joshua Patterson, general manager of data science at NVIDIA

The Future Is AI

Healthcare experts will discuss how AI is changing the industry to provide better service and patient outcomes in a panel moderated by Kimberly Powell, vice president of healthcare at NVIDIA.

They’ll share examples of how they’ve built programs for AI in healthcare and present strategies for using AI to accelerate the improvement of healthcare quality, cost and access.

Panelists include:

  • Gil Alterovitz, director of AI at the U.S. Department of Veterans Affairs
  • Susan Gregurick, director of the biophysics, biomedical technology, and computational biosciences division at the National Institutes of Health
  • Jorge Cardoso, CTO at the London Medical Imaging and AI Centre

AI is also changing the future of the workforce, which business leaders will discuss in a panel moderated by Tonie Hansen, who heads corporate social responsibility at NVIDIA.

Panelists will focus on how sensible policies can help create opportunities for current and future generations of workers. They’ll share tangible advice on reskilling and upskilling employees into data science and IT roles, and preparing computer scientists for AI and machine learning, concentrating on how to do so across socioeconomic, racial and ethnic groups for a more diverse workforce.

Panelists include:

  • Laura Montoya, founder and managing partner at Accel AI
  • Charles Eaton, executive vice president of social innovation at CompTIA
  • Rhonda Foxx, former chief of staff for U.S. Representative Alma Adams of North Carolina

View descriptions of these AI policy panels in more detail on the GTC DC website and register for the conference. Media may request a complimentary pass here.

The post Top Experts from Government, Industry Join to Take On Critical AI Issues at GTC DC appeared first on The Official NVIDIA Blog.

[D] Lex Fridman deletes Siraj Podcast episode and scrubs his site and social media of all mentions of Siraj.

Written on . Posted in Reddit MachineLearning.

[D] Hybrid Collaborative Filtering Recommender

Written on . Posted in Reddit MachineLearning.

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.

submitted by /u/RatataUbuntu
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[D] How to use machine learning to group events?

Written on . Posted in Reddit MachineLearning.

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?

submitted by /u/jgonzalezferrer
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[N] Test a Distilled GPT-2’s generative capabilities

Written on . Posted in Reddit MachineLearning.

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.

submitted by /u/jikkii
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