Collaborating Authors

r/MachineLearning - [D] What deep learning papers should I implement to learn?


"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.



Building on the founders' pioneering research in deep imitation learning, deep reinforcement learning and meta-learning, Embodied Intelligence is developing AI software (aka robot brains) that can be loaded onto any existing robots. While traditional programming of robots requires writing code, a time-consuming endeavor even for robotics experts, Embodied Intelligence software will empower anyone to program a robot by simply donning a VR headset and guiding a robot through a task. These human demonstrations train deep neural nets, which are further tuned through the use of reinforcement learning, resulting in robots that can be easily taught a wide range of skills in areas where existing solutions break down. Complicated tasks like the manipulation of deformable objects such as wires, fabrics, linens, apparel, fluid-bags, and food; picking parts and order items out of cluttered, unstructured bins; completing assemblies where hard automation struggles due to variability in parts, configurations, and individualization of orders, are all candidates to benefit from Embodied Intelligence's work.

AI, Reinforcement Learning, Neural Networks...DevOps?


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.

Rubik's Code Artificial Intelligence Without Tears


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.