This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step one--learning how to get a GPU server online suitable for deep learning--and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems. There are around 20 hours of lessons, and you should plan to spend around 10 hours a week for 7 weeks to complete the material. The course is based on lessons recorded during the first certificate course at The Data Institute at USF.

One does not simply walk into TensorFlow. A PhD is a good opportunity for introspection. In fact, it is important to create opportunities for introspection no matter how busy or insignificant the present feels like. We should not regard our past as an immature period, but as an unfolding story. A story of discoveries, mistakes, skills, and projects that are now part of our professional consciousness.

In today's blog post you are going to learn how to perform face recognition in both images and video streams using: As we'll see, the deep learning-based facial embeddings we'll be using here today are both (1) highly accurate and (2) capable of being executed in real-time. To learn more about face recognition with OpenCV, Python, and deep learning, just keep reading! Inside this tutorial, you will learn how to perform facial recognition using OpenCV, Python, and deep learning. We'll start with a brief discussion of how deep learning-based facial recognition works, including the concept of "deep metric learning". From there, I will help you install the libraries you need to actually perform face recognition. Finally, we'll implement face recognition for both still images and video streams. As we'll discover, our face recognition implementation will be capable of running in real-time.

Eastern China's Anhui province plans to strengthen research and development into artificial intelligence to value the regional sector at over CNY15 billion (USD2.3 billion) by 2020. The program will center around an industry park specialized in speech recognition, China Speech Valley, in the provincial capital Hefei, state-owned news agency Xinhua reported. The scheme will support companies, universities and research institutes to broaden applications in agriculture, manufacturing, education, medicine and urban management. "China Speech Valley will provide the scientific and education resources," said Qi Dongfeng, head of operations at the park. "The program will focus on industrial applications up and down the supply chain, with research directed at chips, algorithms, smart voice products and intelligent sensors."

Or is it man and machines? Learn all about artificial intelligence in a free live webinar hosted by the National Press Foundation at 1 p.m. ET on Tuesday, June 19. We'll help you understand AI and how it works, offer up examples of everyday applications of AI, discuss how the workforce will change with AI, and look at what's coming next in this rapidly changing science. This webinar is sponsored by IBM.

In a world dominated by data, Data Science is the ladder to building a promising career in unique and challenging job positions. Kickstarting your career in Data Science is now easier than ever thanks to the vast pool of online platforms offering Data Science courses. These courses are specially designed to walk you through the concepts and intricacies of Data Science. But, do you know the exact way to climb the ladder? Fret not, for we're here to show you how! So, let's begin, shall we?

Sentence simplification aims to improve readability and understandability, based on several operations such as splitting, deletion, and paraphrasing. However, a valid simplified sentence should also be logically entailed by its input sentence. In this work, we first present a strong pointer-copy mechanism based sequence-to-sequence sentence simplification model, and then improve its entailment and paraphrasing capabilities via multi-task learning with related auxiliary tasks of entailment and paraphrase generation. Moreover, we propose a novel 'multi-level' layered soft sharing approach where each auxiliary task shares different (higher versus lower) level layers of the sentence simplification model, depending on the task's semantic versus lexico-syntactic nature. We also introduce a novel multi-armed bandit based training approach that dynamically learns how to effectively switch across tasks during multi-task learning. Experiments on multiple popular datasets demonstrate that our model outperforms competitive simplification systems in SARI and FKGL automatic metrics, and human evaluation. Further, we present several ablation analyses on alternative layer sharing methods, soft versus hard sharing, dynamic multi-armed bandit sampling approaches, and our model's learned entailment and paraphrasing skills.

Jeff Maggioncalda was recently named CEO of Coursera. I have interviewed both founders of the company, Andrew Ng and Daphne Koller, so I was curious about Maggioncalda's perspective on the company, education technology and the massive open online courses more generally, and his own background as an entrepreneur. Regarding the last point, Maggioncalda was previously the CEO of Financial Engines Inc, a company co-founded by economist and Nobel Prize winner William Sharpe and recently sold for $3 billion. During his 18 years as CEO of Financial Engines Inc, Maggioncalda had to pivot three times from his original idea before becoming a success. Financial Engines would go on to beocme the largest independent online retirement advice platform with more than $100b under management.

What Treasury and Finance professionals need to know to be competitive in the Fourth Industrial Revolution of AI, machine learning, robotic process automation (RPA), and emerging tech. We have a need for smarter and faster decision making. It stands to reason, then, that more data would create more informed decisions. Together, they are the what (data) and how (machine learning) of solving complex problems faster and smarter. While the term is certainly in market, most current technology solutions are machine learning, a subset of AI.

This blog post survey the attacks techniques that target AI (artificial intelligence) systems and how to protect against them. This post explores each of these classes of attack in turn, providing concrete examples and discussing potential mitigation techniques. This post is the fourth, and last, post in a series of four dedicated to providing a concise overview of how to use AI to build robust anti-abuse protections. The first post explained why AI is key to building robust protection that meets user expectations and increasingly sophisticated attacks. Following the natural progression of building and launching an AI-based defense system, the second post covered the challenges related to training classifiers. The third one looked at the main difficulties faced when using a classifier in production to block attacks.