Udemy is the biggest website in the world that offer courses in many categories, all the skills that you would be looking for are offered in Udemy, including languages, design, marketing and a lot of other categories, so when you ever want to buy a courses and pay for a new skills, Udemy would be the best forum for you. You can find payment courses, 100 free courses and coupons also, more than 12 categories are offered, and that what makes sure you will find the domain and the skill you are looking for. Our duty is to search for 100 off courses and free coupons. Have you never learned coding before and want to learn the basics of programming and Object Oriented Programming? Are you confused about the basics of Object Oriented Programming?

A large number of corporations are moving toward the field of data science and machine learning. There are industries ranging from pharmaceuticals, retail, manufacturing, and automobile industries that are seeking ways to promote their products and services with the use of intelligent systems driven by artificial intelligence. To make things interesting, they are being used in the development of software for self-driving vehicles that are going to take the world by surprise in the next 2–3 years. In light of this, it is important to learn the most important technologies and innovations taking place, especially in the field of automation. To learn these new technologies and tools, there are a massive number of online courses that teach the fundamentals along with practical use cases.

Causal machine learning is a field that focuses on using machine learning method to tackle causality problems. Despite the recent progress of this field, there are still many unresolved challenges including missing data, selection bias, unobserved confounders, etc., which are ubiquitous in the real world. Advances in any of the above areas can greatly reduce the gap between research and real world impact. In this competition, we focus on two fundamental challenges of causal machine learning in the context of education using time-series data. The first is to identify the causal relationships between different constructs, where a construct is defined as the smallest element of learning. The second challenge is to predict the impact of learning one construct on the ability to answer questions on other constructs. Addressing these challenges will not only impact the causal ML community but also enable optimisation of students' knowledge acquisition, which can be deployed in a real edtech solution impacting millions of students. Participants will run these tasks in an idealised environment with synthetic data and a real-world scenario with evaluation data collected from a series of A/B tests. We expect participants to develop novel machine learning methodologies for causal discover between different constructs and the impact estimation of learning one construct on other constructs, which should bring fundamental advances to causal ML.

In this article you will learn how to make a prediction from a time series with Tensorflow and Keras in Python. We will use a sequential neural network created in Tensorflow based on bidirectional…

The best deep learning books provide an excellent learning experience for beginners and experts alike. These books are a great way to learn how to apply deep learning techniques to natural language processing tasks. To get started, you can start by reading this book, which is perfect for beginners and intermediate Python users. The book also introduces you to some of the most important topics in NLP and deep learning. The Grookking Deep Learning books are designed to teach the principles of deep learning.

Gradient Descent is the most widely used optimization strategy in machine learning and deep learning. Whenever the question comes to train data models, gradient descent is joined with other algorithms and ease to implement and understand. There is a common understanding that whoever wants to work with the machine learning must understand the concepts in detail. This article will also try to curate the information available with us from different sources, as a result, you will learn the basics. This week, I have got a task in my MSc AI course on gradient descent. If you are new to this journal, Open Tech Talks is your weekly sandbox for technology insights, experimentation, and inspiration with the primary objective of learning and sharing.

This paper considers distributed resource allocation and sum-preserving constrained optimization over lossy networks, where the links are unreliable and subject to packet drops. We define the conditions to ensure convergence under packet drops and link removal by focusing on two main properties of our allocation algorithm: (i) The weight-stochastic condition in typical consensus schemes is reduced to balanced weights, with no need for readjusting the weights to satisfy stochasticity. (ii) The algorithm does not require all-time connectivity but instead uniform connectivity over some non-overlapping finite time intervals. First, we prove that our algorithm provides primal-feasible allocation at every iteration step and converges under the conditions (i)-(ii) and some other mild conditions on the nonlinear iterative dynamics. These nonlinearities address possible practical constraints in real applications due to, for example, saturation or quantization among others. Then, using (i)-(ii) and the notion of bond-percolation theory, we relate the packet drop rate and the network percolation threshold to the (finite) number of iterations ensuring uniform connectivity and, thus, convergence towards the optimum value.

The ever growing abundance of learning traces in the online learning platforms promises unique insights into the learner knowledge assessment (LKA), a fundamental personalized-tutoring technique for enabling various further adaptive tutoring services in these platforms. Precise assessment of learner knowledge requires the fine-grained Q-matrix, which is generally designed by experts to map the items to skills in the domain. Due to the subjective tendency, some misspecifications may degrade the performance of LKA. Some efforts have been made to refine the small-scale Q-matrix, however, it is difficult to extend the scalability and apply these methods to the large-scale online learning context with numerous items and massive skills. Moreover, the existing LKA models employ flexible deep learning models that excel at this task, but the adequacy of LKA is still challenged by the representation capability of the models on the quite sparse item-skill graph and the learners' exercise data. To overcome these issues, in this paper we propose a prerequisite-driven Q-matrix refinement framework for learner knowledge assessment (PQRLKA) in online context. We infer the prerequisites from learners' response data and use it to refine the expert-defined Q-matrix, which enables the interpretability and the scalability to apply it to the large-scale online learning context. Based on the refined Q-matrix, we propose a Metapath2Vec enhanced convolutional representation method to obtain the comprehensive representations of the items with rich information, and feed them to the PQRLKA model to finally assess the learners' knowledge. Experiments conducted on three real-world datasets demonstrate the capability of our model to infer the prerequisites for Q-matrix refinement, and also its superiority for the LKA task.

TMIC is an App Inventor extension for the deployment of ML models for image classification developed with Google Teachable Machine in educational settings. Google Teachable Machine, is an intuitive visual tool that provides workflow-oriented support for the development of ML models for image classification. Aiming at the usage of models developed with Google Teachable Machine, the extension TMIC enables the deployment of the trained models exported as TensorFlow.js to Google Cloud as part of App Inventor, one of the most popular block-based programming environments for teaching computing in K-12. The extension was created with the App Inventor extension framework based on the extension PIC and is available under the BSD 3 license. It can be used for teaching ML in K-12, in introductory courses in higher education or by anyone interested in creating intelligent apps with image classification. The extension TMIC is being developed by the initiative Computa\c{c}\~ao na Escola of the Department of Informatics and Statistics at the Federal University of Santa Catarina/Brazil as part of a research effort aiming at introducing AI education in K-12.

In Federated Learning (FL) of click-through rate (CTR) prediction, users' data is not shared for privacy protection. The learning is performed by training locally on client devices and communicating only model changes to the server. There are two main challenges: (i) the client heterogeneity, making FL algorithms that use the weighted averaging to aggregate model updates from the clients have slow progress and unsatisfactory learning results; and (ii) the difficulty of tuning the server learning rate with trial-and-error methodology due to the big computation time and resources needed for each experiment. To address these challenges, we propose a simple online meta-learning method to learn a strategy of aggregating the model updates, which adaptively weighs the importance of the clients based on their attributes and adjust the step sizes of the update. We perform extensive evaluations on public datasets. Our method significantly outperforms the state-of-the-art in both the speed of convergence and the quality of the final learning results.