All these 3 examples are available on my personal website, in case you are interested in testing them out. In this article, I will walk you through how to realize the first of these three examples. All the code and datasets used to create these examples are available on my GitHub repository. For this example, I will make use of this "Swedish Committee on Analysis of Risk Premium in Motor Insurance" dataset. This simple dataset is composed of just two columns ( X number of claims and Y total payment for all the claims in thousands of Swedish Kronor for geographical zones in Sweden). As part of this demonstration, we will try to predict the total payment for all the claims by examining the total number of claims distribution.

The past 10 years wouldn't have been possible without you. Information technology and software services company Softclick Investments is hosting an Artificial Intelligence (AI) for Business course from the 24th of September to 26 October at Batanai Gardens. The course is supposed to provide "practical, comprehensive training that enables participants to immediately and effectively partake in enterprise AI projects." The courses require no technical background and will be open to all "executives and professionals from all functions across all industries." At the end of the course, participants will earn a certificate.

Created by 365 Careers 5.5 hours on-demand video course This course will teach you the practical skills that would allow you to land a job as a quantitative finance analyst, a data analyst or a data scientist. In no time, you will acquire the fundamental skills that will enable you to perform complicated time series analysis directly applicable in practice. This course is exactly what you need to comprehend time series once and for all. Not only that, but you will also get a ton of additional materials – notebooks files, course notes, quiz questions, and many, many exercises – everything is included.

Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples only, typical meta-learning models use shallow neural networks, thus limiting its effectiveness. In order to achieve top performance, some recent works tried to use the DNNs pre-trained on large-scale datasets but mostly in straight-forward manners, e.g., (1) taking their weights as a warm start of meta-training, and (2) freezing their convolutional layers as the feature extractor of base-learners. In this paper, we propose a novel approach called meta-transfer learning (MTL) which learns to transfer the weights of a deep NN for few-shot learning tasks. Specifically, meta refers to training multiple tasks, and transfer is achieved by learning scaling and shifting functions of DNN weights for each task. In addition, we introduce the hard task (HT) meta-batch scheme as an effective learning curriculum that further boosts the learning efficiency of MTL. We conduct few-shot learning experiments and report top performance for five-class few-shot recognition tasks on three challenging benchmarks: miniImageNet, tieredImageNet and Fewshot-CIFAR100 (FC100). Extensive comparisons to related works validate that our MTL approach trained with the proposed HT meta-batch scheme achieves top performance. An ablation study also shows that both components contribute to fast convergence and high accuracy.

An AI-based eLearning platform is a machine/system that possesses the ability to perform different tasks requiring human intelligence. It maintains the ability to create solutions to human-related problems, like speech recognition, translations involving different languages, decision making, and much more. Even in our mobile devices, an Artificial Intelligence engine is incorporated to help with studying our patterns in order to create likely suggestions during texting. Even though the AI-based eLearning platform hasn't become a standard learning approach amidst most learning organizations, there's a need for it. Although Artificial Intelligence is not of much use, it's on the way to making a positive contribution toward the effectiveness of eLearning training.

Sign in to report inappropriate content. We've been working on calculating the regression, or best-fit, line for a given dataset in Python. Previously, we wrote a function that will gather the slope, and now we need to calculate the y-intercept.

Artificial intelligence and machine learning haven't just grabbed headlines and made for blockbuster movies; they're poised to make a real difference in our everyday lives, such as with self-driving cars and life-saving medical devices. In fact, according to Global Big Data Conference, AI is "completely reshaping life sciences, medicine, and healthcare" and is also transforming voice-activated assistants, image recognition and many other popular technologies. Artificial Intelligence is a term used for machines that can interpret the data, learn from it, and use it to do such tasks that would otherwise be performed by humans. Machine Learning is a branch of Artificial Intelligence which focuses more on training the machines to learn on their own without much supervision. What is a neural network?

While recent advances in language processing with Deep Neural Networks (DNNs) present high-quality translation and classification of the texts, the Holy Grail of the language learning remains missed. That is, while humans appear capable to acquire languages in unsupervised way based on everyday conversations easily, the DNNs require extensive supervised training. Moreover, the humans are capable to acquire explainable and reasonable rules of connecting words into sentences based on grammatical rules and conversational patterns and have the grammatical and semantic categories of words well understood, with all that synonyms and homonyms. On the opposite, the very advanced DNN models remain black boxes not being understandable and inspectable. That is why we are looking for Understandable Language Processing (ULP) which would let acquisition of the language, comprehension of textual communications and production of textual messages in reasonable and transparent way.

This podcast interview focuses on what's real and not real in the world of AI, and my guest is Daniel Faggella, founder and CEO of Emerj AI Research Called upon by organizations like the World Bank, the United Nations, INTERPOL, and global pharmaceutical and banking companies, Emerj CEO Daniel Faggella helps business and government leaders navigate the competitive landscape of AI capabilities, and build strategies that win. His company, Emerj, helps governments and enterprises reduce risk and maximize the bottom-line impact of artificial intelligence capabilities. They map the capability-space of AI across major sectors (banking, pharma, retail, etcetera), helping leaders see what's possible, what's working i.e., where's real ROI and traction, and what to do about it. Being an active reader of his weekly update on the world of AI and got inspired by Daniels down to earth and challenger view on the topic. This is why I invited him to my podcast.

The Conference Board of Canada's Global Commerce Centre (GCC) held a strategic foresight workshop on November 19, 2018. The workshop allowed GCC stakeholders to discuss and develop a series of plausible futures with specific assumptions on AI (artificial intelligence) global adoption, and the openness of the global trade environment. This strategic foresight report identifies four potential futures for consideration. The report provides insights into the challenges and opportunities that industries, the government, and the public may face as AI technologies and global economic trends continue to evolve. All four workshop groups highlighted the role of government policies and the need for good governance, ethical frameworks, and educational programs.