Learn the basics of Deep Learning with hands on exercises using the Caffe deep learning framework and the DIGITS visual interface. Build your own model and start classifying images. Artificial intelligence, machine learning and deep learning are in the news and all around us. They give us the promise of computers solving tasks that until recently were very hard for computers: speech recognition, translation, object recognition, image classification, autonomous driving cars. Caffe framework is free, open sourced, continuously improved, has good documentation and even has an entire zoo of pre trained deep neural network models for image classification and other computer vision tasks.

In second place, Ng saw neither unsupervised learning nor reinforcement learning, but transfer learning. One of the hottest developments within Deep Learning was Generative Adversarial Networks (GANs). Secondly, end-to-end (supervised) Deep Learning allows us to learn to map from inputs directly to outputs. The Conference on Neural Information Processing Systems (NIPS) is one of the two top conferences in machine learning. Among ML research areas, supervised learning is the undisputed driver of the recent success of ML and will likely continue to drive it for the foreseeable future.

This is just one technique that can be used to learn about the physical world, Tenenbaum says. A range of new three-dimensional environments aimed at AI researchers should drive further research in this area (see "A 3-D World for Smarter AI Agents" and "New Tool Lets AI Learn to Do Almost Anything on a Computer"). A team from the University of California, Berkeley, led by Sergey Levine, presented a system that learns about the physical world using a combination of video imagery and experimentation. This offers a way to develop and test simple ideas that might eventually transfer to the real world. This is just one technique that can be used to learn about the physical world, Tenenbaum says.