Despite being quite effective in a variety of tasks across industries, deep learning is constantly evolving, proposing new neural network (NN) architectures such as the Spiking Neural Network (SNN). This exciting course introduces you to the next generation of Machine Learning. You would be able to learn about the fundamentals of Spiking Neural Networks and Brain-Computer Interfacing (BCI). This course has the rigour enough to enable you not only to understand BCI but its implementation in spiking neural networks and to apply these concepts to Brain Healthcare (IT) even on edge machines using Tiny ML. TinyML is a field of study in Machine Learning and Embedded Systems that explores the types of models you can run on small, low-powered devices like microcontrollers.

This course will provide a prospect for participants to establish or progress their considerate on the Genetic Algorithms, GANs and Variational Auto- encoders and their implementation in Python framework. This course encompasses algorithm processes, approaches, and application dimensions. Genetic algorithm which reflects the process of natural selection though selection of fittest individuals is explained thoroughly. Further its implementation in Python Library is exhibited step- wise. Similarly, Generative Adversarial Networks, or GANs for short, are introduced as an approach to generative modelling.

You will need a laptop computer with specific software installed prior to the session. When you register for the class, you will receive detailed instructions for software download and installation. R is the one of the most popular machine learning tools in use today. This course focuses on taking concepts in machine learning and applying them in practical ways. Common algorithms such as regression, clustering, and classification are explained, applied, and evaluated using R. Participants will complete exercises to solidify understanding and build skills with the intent of finishing the course with a toolkit that can be used to build R machine learning skills.

In the last two decades, technology has grown progressive and futuristic through several advancements. Artificial intelligence -- AI is perhaps the future of technology. Lately, many programmers and developers show immense interest in adopting AI. In this article, we will be highlighting what artificial intelligence for beginners looks like. You will also learn the basics of Artificial Intelligence which would give you an equitable idea about this emerging technology.

Are you looking for FREE Math Courses for Data Science? If yes, then this article is for you. In this article, you will find the 7 FREE Math Courses on Udacity for Data Science. These free courses will help you to learn statistics, probability, linear algebra, and calculus. All courses are completely free.

Our mission: to help people learn to code for free. We accomplish this by creating thousands of videos, articles, and interactive coding lessons - all freely available to the public. We also have thousands of freeCodeCamp study groups around the world. Donations to freeCodeCamp go toward our education initiatives, and help pay for servers, services, and staff. You can make a tax-deductible donation here.

Traditional learning-based approaches to student modeling generalize poorly to underrepresented student groups due to biases in data availability. In this paper, we propose a methodology for predicting student performance from their online learning activities that optimizes inference accuracy over different demographic groups such as race and gender. Building upon recent foundations in federated learning, in our approach, personalized models for individual student subgroups are derived from a global model aggregated across all student models via meta-gradient updates that account for subgroup heterogeneity. To learn better representations of student activity, we augment our approach with a self-supervised behavioral pretraining methodology that leverages multiple modalities of student behavior (e.g., visits to lecture videos and participation on forums), and include a neural network attention mechanism in the model aggregation stage. Through experiments on three real-world datasets from online courses, we demonstrate that our approach obtains substantial improvements over existing student modeling baselines in predicting student learning outcomes for all subgroups. Visual analysis of the resulting student embeddings confirm that our personalization methodology indeed identifies different activity patterns within different subgroups, consistent with its stronger inference ability compared with the baselines.

Interestingness recognition is crucial for decision making in autonomous exploration for mobile robots. Previous methods proposed an unsupervised online learning approach that can adapt to environments and detect interesting scenes quickly, but lack the ability to adapt to human-informed interesting objects. To solve this problem, we introduce a human-interactive framework, AirInteraction, that can detect human-informed objects via few-shot online learning. To reduce the communication bandwidth, we first apply an online unsupervised learning algorithm on the unmanned vehicle for interestingness recognition and then only send the potential interesting scenes to a base-station for human inspection. The human operator is able to draw and provide bounding box annotations for particular interesting objects, which are sent back to the robot to detect similar objects via few-shot learning. Only using few human-labeled examples, the robot can learn novel interesting object categories during the mission and detect interesting scenes that contain the objects. We evaluate our method on various interesting scene recognition datasets. To the best of our knowledge, it is the first human-informed few-shot object detection framework for autonomous exploration.

Description Problem Solving Using Computational Thinking is a training course on computer thinking and the process of solving physical problems with software solutions and programming, published by Corsara Training Academy. One of the misconceptions about computers and computer systems is that they are thought of. Computers can not think like humans, but it is possible to set some commands for the computer and then teach the computer how to do these commands. The process of determining the command for the computer and specifying the steps to execute it is called programming. Before starting the programming and coding process, programmers must be familiar with exactly the commands and goals of their software and then express them in the form of comprehensible commands for the computer.

Since 2016, Data Science has remain the sexiest and the most sort after career in the world. Data Scientist command huge salaries and many companies spend most of their budget hiring skilled Data Scientist to optimize their products. Over the past 2-3 years, getting a Data Science job has become very competitive due to its lucrative salaries and working conditions. A lot more people now desire to be Data Scientist than any other field making the field even more competitive. Recruiters have changed their strategies of recruiting candidates for Data Science roles.