There are numerous on-line and off-line technical resources about deep learning. Everyday people publish new papers and write new things. However, it is rare to see resources teaching practical concerns for structuring a deep learning projects; from top to bottom, from problem to solution. People know fancy technicalities but even some experienced people feel lost in the details, once they need to structure their own project. It is not possible to give a single pill ruling the all but this course establishes a good basis to think about a DL project.

Can machine intelligence in the form of Amazon Macie andGoogle Cloud DLP API solve once impossible problems? Deep learning AI algorithms have revolutionized natural language processing (NLP) and automated image analysis and enable features that were once the stuff of science fiction that now seem as routine. Whether it's online text translation, consumer chatbots or automatic face detection and tagging in photos, predictive analytics and deep learning enable features once seen as impossible. As I've discussed many times over the past few months, whether for cyber security like malware detection, conversational UIs, or specialized industry applications, AI is reshaping the world of enterprise software, with significant implications for every business. One area of emerging promise for machine intelligence enhancement is a vexing problem facing every organization; data protection and privacy.

Several choices can be considered with some geared towards a research-focused approach whilst others are built with an aim for use in industrial applications. Much like choosing a programming language, the best candidate often depends on what it will be used for. For example, while using MATLAB or Octave may be easier to experiment with Machine Learning and Deep Learning architectures, they are poorly suited for deploying to production. Ultimately, the choice of language will hinge on the problem that needs to be solved. The most popular language used in ML is Python, a lot of examples you will see in tutorials will be Python, and several popular deep learning libraries primarily support Python (Google's TensorFlow, Keras, Theano) and a large number of Data Scientists seem to favor Python. Moreover, this year's IEEE Spectrum ranking of top programming has Python at the top spot with C and Java following closely.

Have you ever wondered just what this phrase Deep Learning is referring to and why it matters? If so then this post is for you! In my last post, I demystified a variety of buzzwords, and explained that Deep Learning is a subset of Machine Learning. This post explores the world of deep learning for non-mathematicians. To understand Deep Learning, you must first understand a little about Artificial Neural Networks. I am not going to describe the mathematics behind it all.

People have a weird obsession with games likes Chess and Go. Achievement in them has long been seen as a marker of human intellect, and yet they're among the least human test you could devise; putting players in simplified situations where everything is known, every possible course of action is laid out for them, and the test is one of concentration and logic. We pass far greater tests daily, when we recognise a face in a crowd, when we dynamically balance in motion, when we predict the response our words and expressions will have on another sentient being, or when we do all of the above, effortlessly, at the same time. We don't think of these as challenging because they're so innately human, while playing Chess or Go seems far more impressive precisely because they're more rigid and computational in nature. There's an irony in making a board game one of the'grand challenges' of AI, and it surprises me that more people don't see it.

Machine learning opens the door for the use of training rather than programming in game development. Game development is a complex and labor-intensive effort. Game environments, storylines, and character behaviors are carefully crafted, requiring graphics artists, storytellers, and software engineers to work in unison. Often, games end up with a delicate mix of hard-wired behavior in the form of traditional code and a somewhat more responsive behavior in the form of large collections of rules. Over the last few years, data intensive machine learning (ML) solutions have obliterated rule-based systems in the enterprise--think Amazon, Netflix, and Uber. At Unity, we have explored the use of some of these technologies, including deep learning in content creation and deep reinforcement learning in game development.

AI, or more specifically, the field of Artificial Intelligence research, began at a Dartmouth College workshop in 1956 where computers were winning at checkers, solving algebraic word problems, proving logical theorems, and speaking English. However, these systems were trained by humans to perform these actions and AI research slowed as the difficulties of some of the required remaining tasks (those which would render the machine capable of doing any work that a man can do) were realized. Decreased funding also played a role as financial resources were allocated to more productive projects. AI returned briefly in the 1980's due to the success of expert machines, only to drop off again late in the decade when it once again fell into disrepute. It wouldn't be until the late 90's and early 2000's when (aided by increased computational power) AI would begin to be used for logistics and data mining.

Facebook automatically finds and tags friends in your photos. Google Deepmind's AlphaGo computer program trounced champions at the ancient game of Go last year. Skype translates spoken conversations in real time – and pretty accurately too. Behind all this is a type of artificial intelligence called deep learning. But what is deep learning and how does it work?

The hype surrounding artificial intelligence (AI) is warranted, according to Gartner's latest "Hype Cycle for Emerging Technologies" report. AI is now a "megatrend," barreling its way into the IT mainstream. Currently, a batch of AI technologies like deep learning and machine learning, are perched atop the so-called "Peak of Inflated Expectations," where reality has yet to catch up to their potential. Yet, a confluence of advancements and innovations in computing that will bring an AI-enabled future closer.

We present ease.ml, a declarative machine learning service platform we built to support more than ten research groups outside the computer science departments at ETH Zurich for their machine learning needs. With ease.ml, a user defines the high-level schema of a machine learning application and submits the task via a Web interface. The system automatically deals with the rest, such as model selection and data movement. In this paper, we describe the ease.ml architecture and focus on a novel technical problem introduced by ease.ml regarding resource allocation. We ask, as a "service provider" that manages a shared cluster of machines among all our users running machine learning workloads, what is the resource allocation strategy that maximizes the global satisfaction of all our users? Resource allocation is a critical yet subtle issue in this multi-tenant scenario, as we have to balance between efficiency and fairness. We first formalize the problem that we call multi-tenant model selection, aiming for minimizing the total regret of all users running automatic model selection tasks. We then develop a novel algorithm that combines multi-armed bandits with Bayesian optimization and prove a regret bound under the multi-tenant setting. Finally, we report our evaluation of ease.ml on synthetic data and on one service we are providing to our users, namely, image classification with deep neural networks. Our experimental evaluation results show that our proposed solution can be up to 9.8x faster in achieving the same global quality for all users as the two popular heuristics used by our users before ease.ml.