Cutting Edge Deep Learning For Coders, Part 2 from fast.ai

"Machine learning" and "data science" - two terms that might sound mysterious to someone who has never dealt with these processes before. However, if you have heard about machine learning before, you probably know that it is a process that aims to change the very fundaments of how our society is built... Sounds dramatic? Well, perhaps, but I do have a strong case for that - you'll find it in my machine learning course! Why Choose this Machine Learning Course? Machine learning, as a concept, is indeed a very interesting one.

Many teams try to start an applied AI project by diving into algorithms and data before figuring out desired outputs and objectives. Unfortunately, that's like raising a puppy in a New York City apartment for a few years, then being surprised that it can't herd sheep for you. Instead, the first step is for the owner -- that's you! -- to form a clear vision of what you want from your dog (or ML/AI system) and how you'll know you've trained it successfully. My previous article discussed the why, now it's time to dive into how to do this first step for ML/AI, with all its gory little sub-steps. This reference guide is densely-packed and long, so feel free to stick to large fonts and headings for a two-minute crash course or head straight to the summary checklist version. Cast of characters: decision-maker, ethicist, ML/AI engineer, analyst, qualitative expert, economist, psychologist, reliability engineer, AI researcher, domain expert, UX specialist, statistician, AI control theorist. The tasks we're about to tackle are the responsibility of the project's responsible adult. That's whoever calls the shots.

Machine-learning engineer was the fastest growing job category in the five years to 2017, according to LinkedIn. But tech's hottest role isn't a simple field to break into, requiring at least high school math and some programming knowledge, even to get started. Luckily there are an increasing number of options for those wanting to get a grounding in the field, with Amazon Web Services (AWS) being the latest tech giant to release a set of machine-learning courses for free. That's in addition to the existing well-regarded material available online from the likes of fast.ai and Andrew Ng and Coursera. It's worth noting that most of these courses will benefit from having a basic knowledge of Python and high school linear algebra, statistics and calculus.

With the machine learning market size expected to grow from $1.03 Billion USD in 2016 to $8.81 Billion USD by 2022, it can almost be said that machine learning is taking over the world. With that, there is a growing need for professionals who know the ins and out of machine learning. According to Forbes, machine learning patents grew at a 34 percent Compound Annual Growth Rate (CAGR) between 2013 and 2017, which is the third-fastest growing category of all patents granted. Also, the International Data Corporation (IDC) forecasts that spending on AI and ML will increase from $12 Billion USD in 2017 to $57.6 Billion USD by 2021. Even Deloitte Global predicts that the number of machine learning pilots and implementations will double in 2018 compared to 2017, and double again by 2020.

Machine-learning engineer was the fastest growing job category in the five years to 2017, according to LinkedIn. But tech's hottest role isn't a simple field to break into, requiring at least high school math and some programming knowledge, even to get started. Luckily there are an increasing number of options for those wanting to get a grounding in the field, with Amazon Web Services (AWS) being the latest tech giant to release a set of machine-learning courses for free. That's in addition to the existing well-regarded material available online from the likes of fast.ai and Andrew Ng and Coursera. If you're interested in these courses, it's worth noting that you'll benefit more if you have a basic knowledge of Python and high school linear algebra, statistics, and calculus.

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The Internet has rich and rapidly increasing sources of high quality educational content. Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum creation. We present PREREQ, a new supervised learning method for inferring concept prerequisite relations. PREREQ is designed using latent representations of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a neural network based on the Siamese network architecture. PREREQ can learn unknown concept prerequisites from course prerequisites and labeled concept prerequisite data. It outperforms state-of-the-art approaches on benchmark datasets and can effectively learn from very less training data. PREREQ can also use unlabeled video playlists, a steadily growing source of training data, to learn concept prerequisites, thus obviating the need for manual annotation of course prerequisites.

The seminar on Speech and Language Technology Tools is organized by HunCLARIN, Juhász Gyula Faculty of Pedagogy at the University of Szeged and the Hungarian Association of Applied Linguists and Language. The aim of the seminar is to provide researchers, teachers and students working in the humanities and social sciences a broad view of the corpora and state-of-the-art software in speech and language processing developed for Hungarian, mostly as part of the activities of HunCLARIN. This seminar is supported by CLARIN ERIC. The seminar took place at the Juhász Gyula Faculty of Education, University of Szeged, Hungary on Friday, 19 October, 2018.

HunCLARIN, Juhász Gyula Faculty of Pedagogy at the University of Szeged and the Hungarian Association of Applied Linguists and Language organized a Seminar on Speech and Language Technology Tools. The aim of the seminar is to provide a broad view of the corpora and state-of-the-art software in speech and language processing developed for Hungarian.