We introduce an efficient algorithmic framework for model selection in online learning, also known as parameter-free online learning. Departing from previous work, which has focused on highly structured function classes such as nested balls in Hilbert space, we propose a generic meta-algorithm framework that achieves online model selection oracle inequalities under minimal structural assumptions. We give the first computationally efficient parameter-free algorithms that work in arbitrary Banach spaces under mild smoothness assumptions; previous results applied only to Hilbert spaces. We further derive new oracle inequalities for matrix classes, non-nested convex sets, and $\mathbb{R}^{d}$ with generic regularizers. Finally, we generalize these results by providing oracle inequalities for arbitrary non-linear classes in the online supervised learning model. These results are all derived through a unified meta-algorithm scheme using a novel "multi-scale" algorithm for prediction with expert advice based on random playout, which may be of independent interest.

For the second year in a row, we surveyed a number of great minds in the legal community for their opinions on legal tech. From bitcoin and blockchain to A.I. and chatbots, there's plenty to get excited about. Respondents to this year's survey included: Here's what they had to say: There were several contenders for the biggest legal tech news story of 2017, with A.I. taking a top spot yet again. "The barista at my local Starbucks who thought about going to law school one time was getting ready to launch a legal tech company that focused on A.I. indigent defense via crowdsourcing along the blockchain," said Keith Lee. "That's how prevalent it's been."

About this course: Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars. The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation.

Four Harvard Law students have their heads in the cloud--and they think the rest of the legal profession should join them. With their powerful new search engine called Evisort that harnesses cloud storage and artificial intelligence, they hope to revolutionize the costly and labor-intensive way that lawyers currently handle contracts and other transactional work, liberating them for more creative and interesting tasks. Developed by the students over the past two years, Evisort is "like Google for legal contracts," says Jerry Ting '18, co-founder and CEO, who came up with the idea as an undergraduate. While artificial intelligence is the cutting-edge of automating labor-intensive tasks such as document review, it hasn't yet been widely applied to contracts. Evisort jumps into that gap by enabling lawyers to quickly sort through thousands of contracts and other documents to unlock key insights for transactional work.

Arkansas Gov. Asa Hutchinson has proposed a new organization to foster tech jobs and provide digital education training for residents. Called the Arkansas Partnership for Data Analytics and Computing, the groups was announced on Thursday as a way to coordinate training for residents in data science, coding, machine learning, predictive modeling and computer skills. A report outlining the endeavor says the organization's staff and activities will be funded with up to $25.5 million from public- and private-sector stakeholders that have yet to be named. "My expectation is to set a guide for the state to respond to the needs of our business community, and in doing so, create career opportunities for our best and brightest young workers to remain in Arkansas and raise our overall state capabilities across industry, higher education and government to advance and apply the tools of data analytics and computing," Hutchinson says in the report. Hutchinson's administration cites predictions from Wikibon Research -- showing that the market for big data analytics and computing applications will grow from $18.3 billion in 2014 to $92.2 billion by 2026 -- to justify its investment.

Machine learning (ML) is touted as the most critical skill of current times. Artificial intelligence (AI), an application of ML, is becoming pervasive. From autonomous vehicles to self-tuned databases, AI and ML are found everywhere. Industry analysts often refer to AI-driven automation as the job killer. Almost every domain and industry vertical are getting impacted by AI and ML.

Machine learning (ML) is touted as the most critical skill of current times. Artificial intelligence (AI), an application of ML, is becoming pervasive. From autonomous vehicles to self-tuned databases, AI and ML are found everywhere. Industry analysts often refer to AI-driven automation as the job killer. Almost every domain and industry vertical are getting impacted by AI and ML.

Professor Andrew Ng is the former chief scientist at Baidu, where he led the company's Artificial Intelligence Group. He is an adjunct professor at Stanford University. In 2011 he led the development of Stanford University's main MOOC (Massive Open Online Courses) platform and also taught an online Machine Learning class that was offered to over 100,000 students, leading to the founding of Coursera.

Although deep learning has historical roots going back decades, neither the term "deep learning" nor the approach was popular just over five years ago, when the field was reignited by papers such as Krizhevsky, Sutskever and Hinton's now classic 2012 (Krizhevsky, Sutskever, & Hinton, 2012)deep net model of Imagenet. What has the field discovered in the five subsequent years? Against a background of considerable progress in areas such as speech recognition, image recognition, and game playing, and considerable enthusiasm in the popular press, I present ten concerns for deep learning, and suggest that deep learning must be supplemented by other techniques if we are to reach artificial general intelligence.

This course will help you to learn how to use Google translator API. You will learn how to set up your computer to auto translate your files from one to many different languages. We will learn by translating closed captions or *.vtt files but you can translate any other text. If you have subtitles files for your videos which you want to auto-translate to many different languages then it's the course for you! You will be able to translate those files right away. We will use R software as our programming environment which will allow us to achieve our goal with minimum effort possible. This course is designed for you to quickly achieve your goal to know how to setup your computer in order to automatically translate your text. Join this course because you will get all these additional benefits: You learn to setup Google Cloud Platform API key You will learn how to encrypt and securely use your API key with your scripts You will learn strings manipulations in R You will automate your translating tasks Finally you will be able to make computer doing all job even when you are sleeping!!! Google Translate API is a paid service, however you can use your bonus credit from Google to start learning and applying your knowledge.