In this video from the 2016 HPC Advisory Council Switzerland Conference, Zaikun Xu from the Università della Svizzera Italiana presents: Tutorial Part I: Deep Learning.
“In the past decade, deep learning as a life-changing technology, has gained a huge success on various tasks, including image recognition, speech recognition, machine translation, etc. Pio- neered by several research groups, Geoffrey Hinton (U Toronto), Yoshua Benjio (U Montreal), Yann LeCun(NYU), Juergen Schmiduhuber (IDSIA, Switzerland), Deep learning is a renaissance of neural network in the Big data era.
Neural network is a learning algorithm that consists of input layer, hidden layers and output layers, where each circle represents a neural and the each arrow connection associates with a weight. The way neural network learns is based on how different between the output of output layer and the ground truth, following by calculat- ing the gradients of this discrepancy w.r.b to the weights and adjust the weight accordingly. Ideally, it will find weights that maps input X to target y with error as lower as possible.”
“Deep neural nets are neural networks with many layers. Indeed, the winning solution of 2015 Imagenet competition features a very deep neural network with 152 layers.”
In this video, Zaikun Xu from the Università della Svizzera Italiana presents: Tutorial Part 2: Deep Learning.