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Pan-African Artificial Intelligence and Smart Systems

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This book constitutes the refereed post-conference proceedings of the First International Conference on Pan-African Intelligence and Smart Systems, PAAISS 2021, which was held in Windhoek, Namibia, in September 2021. The 17 revised full papers presented were carefully selected from 41 submissions. The theme of PAAISS 2021 was "Advancing AI research in Africa" and the papers are arranged according to subject areas: Deep Learning; Classification and Pattern Recognition; Neural Networks and Support Vector Machines; Smart Systems.


Traditional vs Deep Learning Algorithms in the Telecom Industry -- Cloud Architecture and Algorithm Categorization

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The unprecedented growth of mobile devices, applications and services have placed the utmost demand on mobile and wireless networking infrastructure. Rapid research and development of 5G systems have found ways to support mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Moreover inference from heterogeneous mobile data from distributed devices experiences challenges due to computational and battery power limitations. ML models employed at the edge-servers are constrained to light-weight to boost model performance by achieving a trade-off between model complexity and accuracy. Also, model compression, pruning, and quantization are largely in place.


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You're looking for a complete Machine Learning and Deep Learning course that can help you launch a flourishing career in the field of Data Science, Machine Learning, Python, R or Deep Learning, right? You've found the right Machine Learning course! Check out the table of contents below to see what all Machine Learning and Deep Learning models you are going to learn. How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.


La veille de la cybersécurité

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April 28, 2022 – Researchers have developed a convolutional neural network (CNN) model, a type of deep learning model, for classifying epileptic seizures that is designed to provide maximum accuracy and minor computational complexity, according to a study published in Soft Computing. The researchers developed their algorithm by integrating CNN architecture with a hierarchical attention mechanism, which was expected to enhance the model's performance. The model comprises three parts: a feature extraction layer, a hierarchical attention layer, and a classification layer. The model, which also uses a support vector machine (SVM) algorithm, analyzes a feature map obtained from the raw EEG signal and determines whether the EEGs it was taken from are "healthy" or "seizure."


Deep Learning Prerequisites: Logistic Regression in Python

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This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.


3D Point Cloud Clustering Tutorial with K-means and Python

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If you are on the quest for a (Supervised) Deep Learning algorithm for semantic segmentation -- keywords alert -- you certainly have found yourself searching for some high-quality labels a high quantity of data points. In our 3D data world, the unlabelled nature of the 3D point clouds makes it particularly challenging to answer both criteria: without any good training set, it is hard to "train" any predictive model. Should we explore python tricks and add them to our quiver to quickly produce awesome 3D labeled point cloud datasets? Let us dive right in! Why unsupervised segmentation & clustering is the "bulk of AI"? Deep Learning (DL) through supervised systems is extremely useful. DL architectures have profoundly changed the technological landscape in the last years.


Which machine learning algorithm should I use?

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This resource is designed primarily for beginner to intermediate data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest. A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is "which algorithm should I use?" Even an experienced data scientist cannot tell which algorithm will perform the best before trying different algorithms. We are not advocating a one-and-done approach, but we do hope to provide some guidance on which algorithms to try first depending on some clear factors. The machine learning algorithm cheat sheet helps you to choose from a variety of machine learning algorithms to find the appropriate algorithm for your specific problems.


Complete Machine Learning & Data Science Bootcamp 2022

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This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000 engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies. You will go from zero to mastery!


Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features

Journal of Artificial Intelligence Research

Time-series data arises in many real-world applications (e.g., mobile health) and deep neural networks (DNNs) have shown great success in solving them. Despite their success, little is known about their robustness to adversarial attacks. In this paper, we propose a novel adversarial framework referred to as Time-Series Attacks via STATistical Features (TSA-STAT). To address the unique challenges of time-series domain, TSA-STAT employs constraints on statistical features of the time-series data to construct adversarial examples. Optimized polynomial transformations are used to create attacks that are more effective (in terms of successfully fooling DNNs) than those based on additive perturbations. We also provide certified bounds on the norm of the statistical features for constructing adversarial examples. Our experiments on diverse real-world benchmark datasets show the effectiveness of TSA-STAT in fooling DNNs for time-series domain and in improving their robustness.


Machine Learning for Forecasting: Size Matters

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Machine learning has been increasingly applied to solve forecasting problems. Classical forecasting approaches, such as ARIMA or exponential smoothing are being replaced by machine learning regression algorithms, such as XGBoost, Gaussian processes or deep learning. However, despite the increasing attention, there are still doubts about the forecasting performance of machine learning methods. Makridakis, one of the most prominent names in the forecasting literature, has recently presented evidence that classical methods systematically outperform machine learning approaches for univariate time series forecasting [1]. This includes algorithms such as the LSTM, multi-layer perceptron or Gaussian processes.