Going to school for a formal degree program for isn't always possible or desirable. For those considering an autodidactic alternative, this is for you. There's too much to learn, and the field is advancing rapidly. Master foundational concepts and then focus on projects in a specific domain of interest -- whether it's natural language understanding, computer vision, deep reinforcement learning, robotics, or whatever else. Motivation is far more important than micro-optimizing a learning strategy for some long-term academic or career goal.

In one Pennsylvania high school, more than 15 languages are spoken in a student body of nearly 4,000. WASHINGTON -- In middle school, Junior Alvarado often struggled with multiplication and earned poor grades in math, so when he started his freshman year at Washington Leadership Academy, a charter high school in the nation's capital, he fretted that he would lag behind. But his teachers used technology to identify his weak spots, customize a learning plan just for him and coach him through it. This past week, as Alvarado started sophomore geometry, he was more confident in his skills. "For me personalized learning is having classes set at your level," Alvarado, 15, said in between lessons.

HUANGCHUAN VILLAGE, CHINA – Every day after lunch, Qu Yexiu used to potter around her house in northwest China doing housework and looking after her 2-year-old grandson. Now, every day after lunch, Qu and her grandson visit the newly opened early-childhood development center in their village of Huangchuan in the mountains of Shaanxi province, where he can play with other toddlers. "Things are better now that we have this village center," said Qu, 56. She looks after her two grandchildren while their parents work and live in nearby Anhui province. The other grandchild attends a preschool.

A vice-chancellor's call for universities to train undergraduates "to tell the machines what to do" has rekindled debate about how higher education institutions can best prepare their students for the jobs of the future. Michael Spence outlined plans for the University of Sydney to move towards offering four-year degrees with a greater focus on problem-solving and cultural competency as sector leaders around the world debate whether the rise of artificial intelligence and automation will require providers to prioritise specialist skills in areas such as coding, or broad knowledge that will allow graduates to adapt to a changing workplace. The shift towards longer degrees also runs counter to the push in the UK for more two-year degrees, designed to allow students to start their career more quickly and more cheaply. In an interview with Times Higher Education, Dr Spence outlined how Sydney had streamlined its 122 degree programmes – a portfolio based on the supposition that "if you enter a narrow tube that has a job name at one end, at the other end you'll plop out into the job" – to just 25. The rise of AI means that such jobs "may not exist by the time you end up there, or at least won't necessarily have any longevity", Dr Spence said.

Distributed machine learning algorithms enable processing of datasets that are distributed over a network without gathering the data at a centralized location. While efficient distributed algorithms have been developed under the assumption of faultless networks, failures that can render these algorithms nonfunctional indeed happen in the real world. This paper focuses on the problem of Byzantine failures, which are the hardest to safeguard against in distributed algorithms. While Byzantine fault tolerance has a rich history, existing work does not translate into efficient and practical algorithms for high-dimensional distributed learning tasks. In this paper, two variants of an algorithm termed Byzantine-resilient distributed coordinate descent (ByRDiE) are developed and analyzed that solve distributed learning problems in the presence of Byzantine failures. Theoretical analysis as well as numerical experiments presented in the paper highlight the usefulness of ByRDiE for high-dimensional distributed learning in the presence of Byzantine failures.

Artificial Intelligence, Machine Learning and Deep Learning are all the rage in the press these days, and if you want to be a good Data Scientist you're going to need more than just a passing understanding of what they are and what you can do with them. There are loads of different methodologies, but for me I would always suggest Artificial Neural Networks as the first AI to learn - but then I've always had a soft spot for ANNs since I did my PhD on them. They've been around since the 1970s, and until recently have only really been used as research tools in medicine and engineering. Google, Facebook and a few others, though, have realised that there are commercial uses for ANNs, and so everyone is interested in them again. When it comes to algorithms used in AI, Machine Learning and Deep Learning, there are 3 types of learning process (aka'training').

Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry - and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I'll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn't. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon.

In this paper, we introduce R-NET, an end-to-end neural networks model for reading comprehension style question answering, which aims to answer questions from a given passage. We first match the question and passage with gated attention-based recurrent networks to obtain the question-aware passage representation. Then we propose a self-matching attention mechanism to refine the representation by matching the passage against itself, which effectively encodes information from the whole passage. We finally employ the pointer networks to locate the positions of answers from the passages. We conduct extensive experiments on the SQuAD and MS-MARCO datasets, and our model achieves the best results on both datasets among all published results.

You may have heard about machine learning from interesting applications like spam filtering, optical character recognition, and computer vision. Getting started with machine learning is long process that involves going through several resources. There are books for newbies, academic papers, guided exercises, and standalone projects. It's easy to lose track of what you need to learn among all these options. So in today's post, I'll list seven steps (and 50 resources) that can help you get started in this exciting field of Computer Science, and ramp up toward becoming a machine learning hero.