You will then learn how to implement these in pythonic and concise PyTorch code, that can be extended to include any future deep Q learning algorithms. These algorithms will be used to solve a variety of environments from the Open AI gym's Atari library, including Pong, Breakout, and Bankheist. You will learn the key to making these Deep Q Learning algorithms work, which is how to modify the Open AI Gym's Atari library to meet the specifications of the original Deep Q Learning papers. Also included is a mini course in deep learning using the PyTorch framework. This is geared for students who are familiar with the basic concepts of deep learning, but not the specifics, or those who are comfortable with deep learning in another framework, such as Tensorflow or Keras.
"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.
This demo follows the description of the Deep Q Learning algorithm described in Playing Atari with Deep Reinforcement Learning, a paper from NIPS 2013 Deep Learning Workshop from DeepMind. The paper is a nice demo of a fairly standard (model-free) Reinforcement Learning algorithm (Q Learning) learning to play Atari games. In this demo, instead of Atari games, we'll start out with something more simple: a 2D agent that has 9 eyes pointing in different angles ahead and every eye senses 3 values along its direction (up to a certain maximum visibility distance): distance to a wall, distance to a green thing, or distance to a red thing. The agent navigates by using one of 5 actions that turn it different angles. The red things are apples and the agent gets reward for eating them.
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.