## How Deep Do You Go? Contributing to the os Package Oliver Stenbom July 25th Gophercon 2019  On an unsuspecting Monday really have to thank them. Round of applause for the community that helps contribute to go. ## How Deep Do You Go? Contributing to the os Package Oliver Stenbom July 25th Gophercon 2019 Let's talk

## VMware SIG Deep Dive into Kubernetes Scheduling Performance and high availability options for vSphere Steve Wong, Michael Gasch KubeCon North America December 13, 2018 ## Presenter Bios ## Steve

Informatique et Recherche 101 Crawfords Corner Road Opérationnelle, Université de Montréal, ## REVIEW ## Deep learning Yann LeCun $ ^{1,2} $ , Yoshua Bengio $ ^{3} $ & Geoffrey Hinton $ ^{4,5} $  Deep learning allows computational models that are composed of multiple processing layers to learn representations visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to

### Prometheus Deep Dive Monitoring. At scale Richard Hartmann & Frederic Branczyk @TwitchiH & @fredbrancz 2018-12-12 ## Who are we? ## Richard "RichiH" Hartmann • Swiss army chainsaw

about a variety of techniques in the last few chapters to improve efficiency and boost the quality of deep learning models. These techniques are just a small subset of the available techniques. It is often of these four options to make an informed decision. Blessed with a large research community, the deep learning field is growing at a rapid pace. Over the past few years, we have seen newer architectures the performance benchmarks higher. Figure 7-1 shows some of the choices we face when working on a deep learning problem in the vision domain for instance. Some of these choices are boolean, others have

Introduction to Efficient Deep Learning Welcome to the book! This chapter is a preview of what to expect in the book. We start off by providing an overview of the state of deep learning, its applications applications, and rapid growth. We will establish our motivation behind seeking efficiency in deep learning models. We will also introduce core areas of efficiency techniques (compression techniques, learning techniques that even if you just read this chapter, you would be able to appreciate why we need efficiency in deep learning models today, how to think about it in terms of metrics that you care about, and finally

a ■ Web：爬虫 • 2016的目标：Web爬虫 + 深度学习 + 自然语言处理 = ? ## 今年最激动人心的事件？ ## Mastering the Game of Go with Deep Neural Networks and Tree Search David Silver $ ^{1*} $ , Aja Huang $ ^{1*} $ , Chris J. Maddison Go that uses value networks to evaluate board positions and policy networks to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games 深度学习关注于端到 端的基于原始数据的学习 ## 为什么需要深度学习？ ## Deep Learning: Why? “I’ve worked all my life in Machine Learning, and I’ve never seen one algorithm knock over benchmarks like Deep Learning” ![Image](/uploads/docu

footprint or quality, we should consider employing suitable efficient architectures. The progress of deep learning is characterized by the phases of architectural breakthroughs to improve on previous results enjoy and so on), without the need of knowing all the encyclopedic data about them. When working with deep learning models and inputs such as text, which are not in numerical format, having an algorithmic inputs should have a larger distance between each other. Embeddings form a crucial part of modern deep-learning models, and we are excited to explain how they work. In the following section we will explain

you'll go." — Dr. Seuss Model quality is an important benchmark to evaluate the performance of a deep learning model. A language translation application that uses a low quality model would struggle with is because, firstly, regularization and dropout are fairly straightforward to enable in any modern deep learning framework. Secondly, data augmentation and distillation can bring significant efficiency let's dive into these learning techniques to understand what they are and how to employ them in deep learning workflows. We start with data augmentation in the next section. ## Data Augmentation Data

to make it shorter.” Blaise Pascal In the last chapter, we discussed a few ideas to improve the deep learning efficiency. Now, we will elaborate on one of those ideas, the compression techniques. Compression a gentle introduction to the idea of compression. Details of quantization and its applications in deep learning follow right after. The quantization section delves into the implementation details using compression might lead to degradation in quality. In our case, we are concerned about compressing the deep learning models. What do we really mean by compressing though? As mentioned in chapter 1, we can break