Python Plays GTA V Part 8

Next steps for #DeepLearning #selfdriving car - #Python Plays #GTAV @Sentdex

  • After the initial release, I got tons of great ideas from all of you, along with some very useful code submissions either in the comments or by a pull request on the Github page.
  • This is a fantastic idea.
  • If you have some ideas, submit a pull request on the Github Project Page or share a gist/text dump…etc.
  • Another idea someone else had, which will be helpful for anyone who struggled with bad FPS before, or moving forward, was that you could use a game mod to slow down the in-game speed/time.
  • This isn’t meant to be a GTA V mods tutorial, but I do want to bring your attention to these trainers, mainly because they make it super easy to create the wanted settings for our agent.

Python Programming tutorials from beginner to advanced on a massive variety of topics. All video and text tutorials are free.
Continue reading “Python Plays GTA V Part 8”

Sergey Brin: I didn’t see AI coming

@Google co-founder Sergey Brin: I didn’t see #AI coming  #wef17

  • Brin said that anyone starting out as a young leader or entrepreneur should focus more on having fun than making money.
  • Google co-founder Sergey Brin: I didn’t see AI coming
  • 3 ways business leaders can use AI ethically
  • How to build an inclusive future in the time of AI
  • British PM Theresa May addresses Davos January 19, 2017 09:19

Sergey Brin, the co-founder of Google and one of the most successful Silicon Valley entrepreneurs, says he did not foresee the artificial intelligence revolution that has transformed the tech industry.
Continue reading “Sergey Brin: I didn’t see AI coming”

The Good, Bad, & Ugly of TensorFlow

The Good, Bad, and Ugly of #TensorFlow. #BigData #DeepLearning #MachineLearning  #AI

  • If you are deploying a model to a cloud environment, you want to know that your model can execute on the hardware available to it, without unpredictable interactions with other code that may access the same hardware.
  • For example, the Udacity tutorials and the RNN tutorial using Penn TreeBank data to build a language model are very illustrative, thanks to their simplicity.
  • For me, holding mental context for a new framework and model I’m building to solve a hard problem is already pretty taxing, so it can be really helpful to inspect a totally different representation of a model; the TensorBoard graph visualization is great for this.
  • But good programmers know it is much harder to write code that humans will use, versus code that a machine can compile and execute.
  • We appreciate their strategy of integrating new features and tests first so early adopters can try things before they are documented.

A survey of six months rapid evolution (+ tips/hacks and code to fix the ugly stuff) from Dan Kuster, one of indico’s deep learning researchers.
Continue reading “The Good, Bad, & Ugly of TensorFlow”