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Author: torontoai

[D] To finish or to “master out”?

Written on . Posted in Reddit MachineLearning.

I’m sure nearly every ML PhD student feels this way at least once or twice throughout their program but as I finish my third year I’m seriously considering going to industry.

I’m happy with my research group, but the pace is slow and I don’t feel like I’m learning much anymore. I’ve spent the past three summers at internships (one at a big industrial research lab, the other two at smaller startups doing research engineer-style work) and I enjoyed the startup environment a lot more than pure research. I’m more much a fan of building systems that do things in the real world than coming up with an architecture that does 0.5% better on CIFAR100.

I would much rather do researchy engineering than “data science” or generic software engineering but I don’t think I have a good shot at one of the research labs-best case is that I graduate in 6 years and then likely end up in one of these research engineer roles that only require an MS. On the one hand I feel like I’d regret not getting a PhD and close some doors, but on the other hand I’m forgoing 3 years of a good salary and spending the remainder of my 20s in a much more interesting location (my university is in a boring college town).

I’m seeing my friends from undergrad grow in their careers (and in their skillsets as engineers) while I sit in the lab 24/7 trying to crank out another paper. The

Those of you who strongly considered leaving (or left) shortly after they got their MS in their program, do you regret it (or do you regret not leaving)?

submitted by /u/throwaway_ml14
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Racing tips from AWS DeepRacer League winners in Stockholm, and AWS DeepRacer TV!

Written on . Posted in Amazon.

The AWS DeepRacer League is the world’s first global autonomous racing league. There are races at 21 AWS Summits globally and select Amazon events, as well as monthly virtual races happening online and open for racing. No matter where you are in the world or your skill level, you can join the league. Get a chance to win AWS DeepRacer cars and the top prize of an all-expenses-paid trip to re:Invent 2019, to compete in the AWS DeepRacer Championship Cup.

Become an AWS DeepRacer racer

The competition is heating up as the Summit Circuit hit the halfway mark in Sweden this week. It was another exciting day of racing at the AWS Summit Stockholm, where all three of our podium finishers came to the summit to compete in the league.

In third place was Charlie, who also raced in the league at the AWS Summit in London on May 8. He secured a top 10 finish, which wins him an AWS DeepRacer car, but wanted to come to Stockholm to try once more to win. In London, he was just 0.8 of a second from the top spot, with a time of 9.7 seconds. With a little more training on his model, he managed to clinch third place in Stockholm with a time of 9.5 seconds. Although he did not win on his second attempt, Charlie is now at the top of the overall summit leaderboard. If the results stay the same, he will get his ticket to re:Invent 2019. Now he’s a pro at the league, so listen to how Charlie approached building his model.

Amy (@cloudreach) was the second-place finisher and the second female to stand on the podium this season, in her second summit race. Like Charlie, earlier this month she competed in London, where her teammate Raul also took second place. Between races, she worked hard on her model and improved her time significantly from 33.2 seconds in London, to 9.25 in Stockholm.

Although she didn’t win, taking part in more than one race has scored her a place on the overall summit leaderboard, giving her another shot at winning a ticket to compete at re:Invent 2019. Learn more about points and prizes to find out how. Here’s a little insight from Amy and one of her teammates on strategy!

In first place was Jouini Luoma, with a time of 8.73 seconds. He works for Cybercom, as a data scientist and AWS DeepRacer racer. Yes, upon his return from sabbatical, his company gave him this new and coveted title! Jouini’s strategy was to build a few models in advance of the race and test each of them out on the track to see how they performed.

He was first in line at 8AM, with six models that he had been training in the AWS DeepRacer console since its launch on April 29. Each was tuned in different ways to give him the best chance to win. His advice? “Keep it simple; do not over complicate it.”

Take a step inside the league with AWS DeepRacer TV

As with all the winners so far, Jouni found success by experimenting with several strategies to apply to his code, to give him the best chance to win. Developers of all skill levels are building their machine learning expertise, and you can now follow your favorites along the way, with the launch of AWS DeepRacer TV.

Episode 1 follows the competition to Amsterdam, featuring Carolinea, Norbert, Kasper, Jesper, and many more developers, all hoping to qualify for a chance to win the Championship Cup at AWS re:Invent 2019. Watch as developers train their models, develop strategies, and discover the potential of machine learning in a fun and competitive environment. Also featured in this episode is the topic of convergence, which is a critical step in the model building process to be ready to race. AWS DeepRacer subject matter expert, Blaine Sundrud, explains more about this topic and some of the basics of competing in the league.

More tips from our experts

The AWS DeepRacer experts are here to help developers through their journey in the league. Sunil Mallya, principal solutions architect at AWS, and also one of the data scientists behind AWS DeepRacer, recently tweeted a tool that helps those who are coming across some common challenges. The logbook analysis tool helps you debug models for a chance to improve lap times and win—both in the virtual and in-person races.

Keep racing, improving models, and scoring points

Points mean prizes! The virtual races are open to all from anywhere in the world. They provide you with multiple chances to win tickets to re:Invent 2019—and you can get started for free, with up to 10 hours of training.

The London Loop race is close to finishing, and a new track opens up on June 1. Fuel up on some racing tips in the developer documentation and be on the lookout for more advice from AWS experts as we head to Chicago and re:MARS for the next in-person AWS DeepRacer events.

About the Author

Alexandra Bush is a Senior Product Marketing Manager for AWS AI. She is passionate about how technology impacts the world around us and enjoys being able to help make it accessible to all. Out of the office she loves to run, travel and stay active in the outdoors with family and friends.

 

 

 

[Project] Looking for an advanced-beginner/intermediate NLP challenge?

Written on . Posted in Reddit MachineLearning.

I’m trying to learn NLP. Most of the available material is “spoon fed” tutorials where you don’t really learn anything, cause you are just doing a by the numbers rehashing of the code. Is there any NLP challenge or data set which is challenging enough that one can actually learn from it, but still suitable to a beginner in the topic (I know other ML methods, especially times series, it’s just NLP that I don’t have any experience in)?

submitted by /u/AlexSnakeKing
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[Discussion] Do any data science practitioners develop their own MCMC models?

Written on . Posted in Reddit MachineLearning.

I gave a tutorial on MCMC methods to some interns last week. And it struck me that although the math is absolutely fascinating, the usefulness of knowing the detailed workings of MCMC was very limited. After all, unless you are part of the PyTorch or TensorFlow dev teams, you really don’t need to know how SGD or ADAM works.

Similarly, you just need to know that your model uses MCMC, but do you really need to know the details of Metropolis-Hastings and or how the cooling schedule works in Simulating Annealing? You would just rely on whatever framework you were using (TFP, PyStan, etc…) to do all that for you, and worry about the high level functional aspects of your model.

​

Have any of you had to write your own MCMC code in a modeling context, not a software development context?

submitted by /u/AlexSnakeKing
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