The rapid growth in natural language processing (NLP) over the last couple years has generated student interest and excitement in learning more about the field. In this paper, we present two types of students that NLP courses might want to train. First, an "NLP engineer" who is able to flexibly design, build and apply new technologies in NLP for a wide range of tasks. Second, an "NLP scholar" who is able to pose, refine and answer questions in NLP and how it relates to the society, while also learning to effectively communicate these answers to a broader audience. While these two types of skills are not mutually exclusive -- NLP engineers should be able to think critically, and NLP scholars should be able to build systems -- we think that courses can differ in the balance of these skills. As educators at Small Liberal Arts Colleges, the strengths of our students and our institution favors an approach that is better suited to train NLP scholars. In this paper we articulate what kinds of skills an NLP scholar should have, and then adopt a backwards design to propose course components that can aid the acquisition of these skills.

In the rapidly evolving field of computer science education, novel approaches to teaching fundamental concepts are crucial for engaging a diverse student body. Given the growing demand for a computing-skilled workforce, it is essential to adapt educational methods to capture the interest of a broader audience than what current computing education typically targets. Engaging educational experiences have been shown to have a positive impact on learning outcomes and examination performance, especially within computing education. Moreover, physical computing devices have been shown to correlate with increased student motivation when students are studying computer science.

Data Analytics is the pursuit of extracting valuable data by using specialized computer systems. The global data analytics market size is anticipated to grow to USD 346.33 Billion by the year 2030 with a CAGR rate of 30.41% according to the Precedence Research report. Data analytics is used to discover and interpret patterns in data for the better optimization of business performance. Let's now deeply discuss the domain. Data analytics is a broad term that is used in the business market, it is important to use tools and methods of data analytics which help in better functioning of business performance.

In this blog, we shall discuss about how to build a neural network to translate from English to German. This problem appeared as the Capstone project for the coursera course Tensorflow 2: Customising your model, a part of the specialization Tensorflow2 for Deep Learning, by the Imperial College, London. The problem statement / description / steps are taken from the course itself. We shall use the concepts from the course, including building more flexible model architectures, freezing layers, data processing pipeline and sequence modelling. Here we shall use a language dataset from http://www.manythings.org/anki/

Last month, I wrote an article on building a data science learning roadmap with free courses offered by MIT. However, the focus of most courses I listed was highly theoretical, and there was a lot of emphasis on learning the math and statistics behind machine learning algorithms. While the MIT roadmap will help you understand the principles behind predictive modelling, what's lacking is the ability to actually implement the concepts learnt and execute a real-world data science project. After spending some time scouring the Internet, I found a couple of freely available courses by Harvard that covered the entire data science workflow?--?from programming to data analysis, statistics, and machine learning. Once you complete all the courses in this learning path, you are also given a capstone project that allows you to put everything you learnt in practice.

Learn about different Activation Functions used in a Neural Network. Implement Artificial Neural Networks to solve real world Problems. Implement the LSTMs to solve Sequential Problems. Learn about different Activation Functions used in a Neural Network. Implement Artificial Neural Networks to solve real world Problems.

Teaching artificial intelligence (AI) is challenging. It is a fast moving field and therefore difficult to keep people updated with the state-of-the-art. Educational offerings for students are ever increasing, beyond university degree programs where AI education traditionally lay. In this paper, we present an experience report of teaching an AI course to business executives in the United Arab Emirates (UAE). Rather than focusing only on theoretical and technical aspects, we developed a course that teaches AI with a view to enabling students to understand how to incorporate it into existing business processes. We present an overview of our course, curriculum and teaching methods, and we discuss our reflections on teaching adult learners, and to students in the UAE.

Using either framework will reduce the headache that is from-scratch development and will allow the engineer/developer to build faster, and create solutions which are more easily explained thanks to respective profilers and TensorBoard. Simply put: there are too many unofficial tutorials covering the same topics and the code quality varies too greatly; these tutorials are noise and can be ignored. TensorFlow, Keras, and PyTorch all offer awesome, free tutorials; and the docs are organized into a textbook like manner. Familiarizing with a study framework like SQ3R, KWT, or PQRST will aid in using the docs to learn about AI/ML while also learning to use the framework. If needing Python standard examples, the site RealPython seems to be reliable and consistent; however, a docs-first research method is best.

One of the co-founders, Martin Nisser, a PhD student from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), explains the digital literacy and self-efficacy focused objectives: "Some of the women haven't had the opportunity to work with a computer for 25 years, and aren't yet accustomed to using the internet. We're working with them to build their capabilities with these modern tools in order to prepare them for life outside," says Nisser. Even for the students who became incarcerated more recently, it can be difficult to keep up with the fast pace of technological advances, since technical programs in correctional facilities are few and far-between. This scarcity of preparatory programs undoubtedly contributes to high and rising recidivism rates: More often than not, those who are released from prison eventually return. While working at TEJI, Nisser had a fortuitous meeting with his two co-founders, Marisa Gaetz (a PhD student from MIT's Department of Mathematics) and Emily Harburg (co-founder of Brave Initiatives, a nonprofit that develops coding bootcamps for young women).

A virtual, autonomous racing challenge launching this week will enable aspiring racers to head to the track without building a car, knowing how to brake and accelerate through a corner, or leaving their computer. And as teams tackle the demands of high-speed and safe driving that pushes race cars to their limits, they will improve the safety of autonomous vehicles and the learning algorithms teaching them to drive. The Learn-to-Race Autonomous Racing Virtual Challenge started Monday, Dec. 6. Competitors use the Learn-to-Race environment to teach an artificially intelligent agent how to race. The challenge is coupled with a workshop on Safe Learning for Autonomous Driving, which is accepting research paper submissions.