For the course "Deep Learning for Business," the first module is "Deep Learning Products & Services," which starts with the lecture "Future Industry Evolution & Artificial Intelligence" that explains past, current, and future industry evolutions and how DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of future industry in the near future. The following lectures look into the hottest DL and ML products and services that are exciting the business world. Then the Amazon Echo and Echo Dot products are introduced along with the Alexa cloud based DL personal assistant that uses ASR (Automated Speech Recognition) and NLU (Natural Language Understanding) technology. The next lecture focuses on LettuceBot, which is a DL system that plants lettuce seeds with automatic fertilizer and herbicide nozzles control. Then the computer vision based DL blood cells analysis diagnostic system Athelas is introduced followed by the introduction of a classical and symphonic music composing DL system named AIVA (Artificial Intelligence Virtual Artist).

Artificial intelligence will play an important role in reshaping an array of major industries such as retail, manufacturing and healthcare. Leading senior executives told the 4th World Internet Conference in Wuzhen, eastern China, that rapid technological changes will transform companies and society. Robin Li, chief executive of Baidu, felt that in comparison with mobile internet technology, which revolutionised consumer services, artificial intelligence (AI) would have a far bigger influence on how companies ran their businesses. "For instance, Baidu is leveraging AI to help supermarkets better manage their supply of fresh food, by analysing and predicting which products are most popular," said Li, who runs China's largest search engine. He pointed out that such solutions had effectively reduced food waste and boosted profit growth at pilot stores.

If farmers want to know how healthy crops are, perhaps they shouldn't trust their eyes. Matt Free -- a manager at Evergreen FS, an agriculture company -- learned that lesson this year. His team provides crop protection services such as fertilizers and herbicides to farmers across Illinois. After a year-long test of a variety of new technologies, Evergreen FS found artificial intelligence could identify trouble, such as fungus growth and water shortages, in corn and soybean crops weeks before the naked eye would ever realize it. The tech, which comes from startup Ceres Imaging, offers farmers an AI analysis of photos taken from planes flying several thousand feet above fields. Previously, the technology was generally limited to orchards and vineyards.

We introduce a generic scheme for accelerating gradient-based optimization methods in the sense of Nesterov. The approach, called Catalyst, builds upon the inexact acceler- ated proximal point algorithm for minimizing a convex objective function, and consists of approximately solving a sequence of well-chosen auxiliary problems, leading to faster convergence. One of the key to achieve acceleration in theory and in practice is to solve these sub-problems with appropriate accuracy by using the right stopping criterion and the right warm-start strategy. In this paper, we give practical guidelines to use Catalyst and present a comprehensive theoretical analysis of its global complexity. We show that Catalyst applies to a large class of algorithms, including gradient descent, block coordinate descent, incremental algorithms such as SAG, SAGA, SDCA, SVRG, Finito/MISO, and their proximal variants. For all of these methods, we provide acceleration and explicit sup- port for non-strongly convex objectives. We conclude with extensive experiments showing that acceleration is useful in practice, especially for ill-conditioned problems.

Drones are sweeping over the global mining industry and for good reason. The future of mining will increasingly rely on the use of drones and automated systems, slashing costs while helping mining companies find and dig up more gold, silver and other metals and minerals.

If farmers want to know how healthy crops are, perhaps they shouldn't trust their eyes. Matt Free -- a manager at Evergreen FS, an agriculture company -- learned that lesson this year. His team provides crop protection services such as fertilizers and herbicides to farmers across Illinois. After a year-long test of a variety of new technologies, Evergreen FS found artificial intelligence could identify trouble, such as fungus growth and water shortages, in corn and soybean crops weeks before the naked eye would ever realize it. The tech, which comes from startup Ceres Imaging, offers farmers an AI analysis of photos taken from planes flying several thousand feet above fields. Previously, the technology was only available for orchards and vineyards.

When considering a startup, especially an early-stage startup, investors want to conduct as much due diligence as possible. What little data they can gather is scattered all over different sources including Crunchbase, LinkedIn, Pitchbooks, company websites, etc. Consolidating this data takes a great amount of time and effort. Furthermore, the data sets can be incomplete or biased depending on the search queries -- imagine overlooking a keyword. To make the due diligence process fairer and less cumbersome for investors, various platforms are using machine learning (ML) to pull together information about startups from all available resources to help investors assess companies and investment opportunities. But where machine learning really shines is in the interplay of data-driven insights that are qualified by human intuition and personal experience.

Did you ever use spell check in google? If you have then you used a machine learning algorithm. There are countless instances in an average person's day where he/she uses machine Learning. It has become a vital component in modern men's life. Driver less cars, Rovers in Mars, Weather predictions, Market share predictions, Speech Processing, Internet of things, Healthcare well these are just the tip of the iceberg.

BHP is applying data science to understand how it services machines located across its mines, in the hope of saving $79 million this financial year alone. The miner revealed plans late last year to set up a maintenance centre of excellence (MCoE) based out of Brisbane. The MCoE will standardise maintenance systems and processes for BHP's worldwide operations, replacing the previous model of having 40 different maintenance organisations globally, each with its own way of working. One of the keys to the MCoE model is its reliance on data science techniques, such as machine learning, to understand how maintenance is performed at each site and where improvements can be made. Like other projects since BHP relaunched its technology function at the start of this year, the idea with the MCoE is to create repeatable processes for its business operations across the world.

The digital revolution has brought with it a new way of thinking about manufacturing and operations. Emerging challenges associated with logistics and energy costs are influencing global production and associated distribution decisions. Significant advances in technology, including big data analytics, AI, Internet of Things, robotics and additive manufacturing, are shifting the capabilities and value proposition of global manufacturing. In response, manufacturing and operations require a digital renovation: the value chain must be redesigned and retooled and the workforce retrained. Total delivered cost must be analyzed to determine the best places to locate sources of supply, manufacturing and assembly operations around the world.