The exponential growth in the number of complex datasets every year requires more enhancement in machine learning methods to provide robust and accurate data classification. Lately, deep learning approaches have achieved surpassing results in comparison to previous machine learning algorithms. However, finding the suitable structure for these models has been a challenge for researchers. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. In short, RMDL trains multiple randomly generated models of Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in parallel and combines their results to produce better result of any of those models individually. In this paper, we describe RMDL model and compare the results for image and text classification as well as face recognition. We used MNIST and CIFAR-10 datasets as ground truth datasets for image classification and WOS, Reuters, IMDB, and 20newsgroup datasets for text classification. Lastly, we used ORL dataset to compare the model performance on face recognition task.

Since he was a child, Derek Paley has been captivated by how shoals of fish move fluidly as a cohesive group, almost as if a single organism. As the Willis H. Young Jr. Professor of Aerospace Engineering Education and director of the Collective Dynamics and Control Laboratory at the University of Maryland, Paley is applying his long-standing source of inspiration to the cooperative control of autonomous vehicles. Fish are particularly interesting for Paley because of their sensory system. He explains that fish have a lateral line system, which is a series of sensors located on their exterior, sometimes appearing on their side as a stripe. With their lateral line sense, fish can perceive the direction and speed of nearby water flow, as well as predators and other obstacles.

In classical programming, software instructions are explicitly made by programmers and nothing is learned from the data at all. In contrast, machine learning is a field of computer science which uses statistical methods to enable computers to learn and to extract knowledge from the data without being explicitly programmed. In this reinforcement learning tutorial, I'll show how we can use PyTorch to teach a reinforcement learning neural network how to play Flappy Bird. But first, we'll need to cover a number of building blocks. Machine learning algorithms can roughly be divided into two parts: Traditional learning algorithms and deep learning algorithms. Traditional learning algorithms usually have much fewer learnable parameters than deep learning algorithms and have much less learning capacity. Also, traditional learning algorithms are not able to do feature extraction: Artificial intelligence specialists need to figure out a good data representation which is then sent to the learning algorithm. Examples of traditional machine learning techniques include SVM, random forest, decision tree, and $k$-means, whereas the central algorithm in deep learning is the deep neural network. The input to a deep neural network can be raw images, and an artificial intelligence specialist doesn't need to find any data representation--the neural network finds the best representation during the training process. A lot of deep learning techniques have been known for a very long time, but recent advances in hardware rapidly boosted deep learning research and development.

Tokyo: The government of Japan is planning to introduce English-speaking Artificial Intelligence (AI) robots in classrooms to help children improve their English speaking skills, considered one of the worst in the world. The Japanese education ministry would be launching a pilot programme to test the effectiveness of the initiative in April 2019, reports Efe news. The initiative will be initially rolled out in 500 schools throughout the country with the aim of fully implementing it in two years, public broadcaster NHK reported Saturday. The programme also includes study apps and online conversation sessions with native English speakers. Japan has proposed improving English skills ahead of the surge in tourists expected during the 2020 Summer Olympics in Tokyo.

I like to keep regular tabs on the state of data science education in America. As one of the hottest fields in the economy, I find that the quality of data science courses is a leading indicator of the state of online education innovation. So, when I see significant new develops in online education technology, I'll dedicate some time to taking the course (and, selfishly, I like to learn new data skills). One website that I've used before, Udacity, recently launched several new courses on artificial intelligence, machine learning and statistics. I've had mixed experiences with Udacity in the past.

This Machine Learning tutorial video is ideal for beginners to learn Machine Learning from scratch. By the end of this tutorial video, you will learn why Machine Learning is so important in our lives, what is Machine Learning, the various types of Machine Learning (Supervised, Unsupervised and Reinforcement learning), how do we choose the right Machine Learning solution, what are the different Machine Learning algorithms and how do they work (with simple examples and use-cases) and finally implement a Machine Learning project/ hands-on demo on Linear Regression Algorithm using Python. You can also go through the Slides here: https://goo.gl/aNmKbQ Machine Learning Articles: https://www.simplilearn.com/what-is-a... To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-... #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people's digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.

Abstract--We propose channel charting (CC), a novel framework in which a multi-antenna network element learns a chart of the radio geometry in its surrounding area. The channel chart captures the local spatial geometry of the area so that points that are close in space will also be close in the channel chart and vice versa. CC works in a fully unsupervised manner, i.e., learning is only based on channel state information (CSI) that is passively collected at a single point in space, but from multiple transmit locations in the area over time. The method then extracts channel features that characterize large-scale fading properties of the wireless channel. Finally, the channel charts are generated with tools from dimensionality reduction, manifold learning, and deep neural networks. The network element performing CC may be, for example, a multi-antenna base-station in a cellular system and the charted area in the served cell. Logical relationships related to the position and movement of a transmitter, e.g., a user equipment (UE), in the cell can then be directly deduced from comparing measured radio channel characteristics to the channel chart. The unsupervised nature of CC enables a range of new applications in UE localization, network planning, user scheduling, multipoint connectivity, handover, cell search, user grouping, and other cognitive tasks that rely on CSI and UE movement relative to the base-station, without the need of information from global navigation satellite systems. UTURE wireless communication systems must sustain a massive increase in traffic volumes, number of terminals, and reliability/latency requirements [2], [3]. C. Studer, S. Medjkouh, and E. Gönültaş are with the School of Electrical and Computer Engineering, Cornell University, Ithaca, NY; email: studer@cornell.edu, T. Goldstein is with the Department of Computer Science, University of Maryland, College Park, MD; email: tomg@cs.umd.edu O. Tirkkonen was a visiting professor at the School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, and is now at the School of Electrical Engineering, Aalto University, Finland; email: olav.tirkkonen@aalto.fi The work of CS, SM, and EG was supported in part by Xilinx Inc., and by the US NSF under grants ECCS-1408006, CCF-1535897, CAREER CCF-1652065, and CNS-1717559.

Humans gather information by engaging in conversations involving a series of interconnected questions and answers. For machines to assist in information gathering, it is therefore essential to enable them to answer conversational questions. We introduce CoQA, a novel dataset for building Conversational Question Answering systems. Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. We analyze CoQA in depth and show that conversational questions have challenging phenomena not present in existing reading comprehension datasets, e.g., coreference and pragmatic reasoning. We evaluate strong conversational and reading comprehension models on CoQA. The best system obtains an F1 score of 65.1%, which is 23.7 points behind human performance (88.8%), indicating there is ample room for improvement. We launch CoQA as a challenge to the community at http://stanfordnlp.github.io/coqa/

Many recent papers address reading comprehension, where examples consist of (question, passage, answer) tuples. Presumably, a model must combine information from both questions and passages to predict corresponding answers. However, despite intense interest in the topic, with hundreds of published papers vying for leaderboard dominance, basic questions about the difficulty of many popular benchmarks remain unanswered. In this paper, we establish sensible baselines for the bAbI, SQuAD, CBT, CNN, and Who-did-What datasets, finding that question- and passage-only models often perform surprisingly well. On $14$ out of $20$ bAbI tasks, passage-only models achieve greater than $50\%$ accuracy, sometimes matching the full model. Interestingly, while CBT provides $20$-sentence stories only the last is needed for comparably accurate prediction. By comparison, SQuAD and CNN appear better-constructed.

There have been several attempts to define a plausible motivation for a chit-chat dialogue agent that can lead to engaging conversations. In this work, we explore a new direction where the agent specifically focuses on discovering information about its interlocutor. We formalize this approach by defining a quantitative metric. We propose an algorithm for the agent to maximize it. We validate the idea with human evaluation where our system outperforms various baselines. We demonstrate that the metric indeed correlates with the human judgments of engagingness.