"A Neural Algorithm of Artistic Style" is very intuitive to understand and not terribly difficult to get going. Plus you don't need crazy hardware as you work with pre-trained models. "Human Level Control Through Deep Reinforcement Learning" is much more complicated, but very rewarding when you get it right as you can watch a machine learn to play your favorite childhood games. And, you'll get a strong grasp of your framework of choice, good debugging techniques, and how to effectively leverage training time on a back-end.
Now, if there is something that data scientists like to do, is merge concepts and create new beautiful and unexpected models. That is why in this article, we will find out what happens when we give the learning agent ability to "see", i.e. what happens when we involve convolutional neural networks into Deep Q-Learning framework.
From CNNs to GPUs, there's a whole spectrum of technologies and tools you can use to bring AI and machine learning into your business. But if you fail to manage your projects correctly, you just won't get the benefits you'd hoped for. That's why at MCubed our speakers don't just dive into the most important concepts and technologies, they show you how to implement them in production and skirt some of the major traps. So as well as covering core concepts and tools such as TensorFLow and Keras, our speakers will be discussing how to make your AI development more efficient, and how you can develop and deploy your machine learning models faster with DevOps. We'll also examine how to avoid vendor lock-in.
Predictions for Artificial Intelligence in 2018 Positive reinforcement Reinforcement learning takes inspiration from the ways that animals learn how certain behaviors tend to result in a positive or negative outcome. Using this approach, a computer can say, figure out how to navigate a maze by trial and error and then associate the positive outcome--exiting the maze--with the actions that led up to it. This lets a machine learn without instruction or even explicit examples. The idea has been around for decades, but combining it with large (or deep) neural networks provides the power needed to make it work on really complex problems (like the game of Go). Through relentless experimentation, as well as analysis of previous games, AlphaGo figured out for itself how to play the game at an expert level.