I wanted to use a Reinforcement Learning Model in order to simulate traffic situations. The agent would learn how to control a network of cars stuck in deep traffic in order to increase efficiency, and reduce the amount of harmful gas released into the air. I want to be able to make a 2d environment that can simulate the potential that Reinforcement Learning has in traffic situations. If I finish this part, and if this next part is feasible, I will do this part.

Extra Part: After making this model, I wanted to make an app, or an extension of google maps, that would be controlled by the agent in real traffic situations. The agent would advise the network of drivers to go at a certain speed and time to increase efficiency, and potentially reduce the amount of accidents made in traffic situations.

Question: How would I create a virtual 2d environment where I could control a car or multiple cars with artificial intelligence. Is there a course that I could take, or a method that should be followed(like using unity machine learning)?

Are there any neural nets trained to calculate the native resolution of a render, pre upscale?

I’m asking specifically because YouTube channels like Digital Foundry are getting stressed out and spending an inordinate amount of time finding the range for dynamic resolutions for modern games, and I was wondering if there were any tools that could be utilized to automate the process.

It would be a great shame if anyone on their team quit due to the stress and tedium that has resulted from dynamic resolution scaling, as they provide invaluable technical information in the field of real time rendering.

Human players are starting to get creative in playing against AlphaStar.

Additional note, AlphaStar is making some very basic mistakes with its unit management:

https://www.youtube.com/watch?v=Di-yRj6TIK8&feature=youtu.be&t=1525

I suggest watching the whole game.

Hello everyone. I started a job in ML half a year ago and found myself to like pytorch the most. I started with 7 years in python beforehand and suppose that influenced my preference. The documentation is great, the functionality clean and simple. I just like the full control over everything and neat features like auto registration of layers in a Module. I also very much love the style of cuda detection during runtime, just pleasant to use. What are your favorites and why ?

I am training a 2d image landmark estimation network where I have lots of synthetic data, but limited realistic labelled data. I can gather lots of un-labelled real data, but labelling is difficult. I have come across a few techniques out there like DANN As a non-expert, I’m not sure where exactly to start. Are there any common techniques that are worth trying first? Thanks!

Hi there!

We are happy to announce the SpeechBrain project, that aims to develop an open-source and all-in-one toolkit based on PyTorch. The goal is to develop a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech systems for speech recognition (both end-to-end and HMM-DNN), speaker recognition, speech separation, multi-microphone signal processing (e.g, beamforming), self-supervised learning, and many others.

The project will be led by Mila (Montréal) and is sponsored by Samsung, Nvidia, and Dolby.

SpeechBrain will also benefit from the collaboration and expertise of other partners such as Avignon Université, Facebook/PyTorch, IBM Research, and Fluent.ai.

Check out https://speechbrain.github.io!

(Also, we are looking for interns 😉 check the website!)

Reddit is an awesome place to discuss, so please, let us know what you would like to see implemented for the speech community! This is a great opportunity to start building an user-friendly and AIO toolkit 😀 !

I have tried a bunch of these types of tools, but can never find one that fits all my criteria. The tool should be:

• Open source
• Specifically made for video annotation
• GUI-based, scrolling through frames and box-annotating objects with ease
• Data can be exported to tensorflow and used to train neural network to recognize objects in similar videos

Does anyone have a video annotation tool that fulfills all these requirements?

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I am trying to make a program where one can input a video, and out comes a list of frames where different pre-made objects were detected.

Unlike usual software dev projects, ML projects have additional huge files like datasets, trained models, label-encodings etc. which can easily go to the size of a few GBs and therefore cannot be tracked using Git.

The article explains how DVC (Data Version Control) tool helps us to version large data files, similar to how we version control source code files using Git and how we can track all the artifacts with DVC — which will make the workflow a lot more productive, as we don’t have to manually keep track of what we did to achieve the state, and also we don’t lose time in the processing of data and building models to reproduce the same state: Version Control for Data Science — Tracking Machine Learning Models and Datasets

I fintuned a pretrained GPT2 model on Trump’s entire twitter archive. Hilarity ensues:

https://twitter.com/botustrump

A colleague commented that it is very on point when it comes to language style, but briefer than the real Trump who often writes longwinded rants…