A team of Carnegie Mellon University learning scientists are developing a tool that could change the way high school teachers and students approach their computer science classes. This month, Schmidt Futures announced that the team is one of the winners of their Futures Forum on Learning: Tools Competition. This award will fund tools to aid recovery from pandemic learning loss and advance the field of learning engineering. The tool, RecapCS, was created by Ember Liu and Neil Thawani with support from John Stamper, an assistant professor in the Human-Computer Interaction Institute. Liu and Thawani both graduated from the HCII's Master of Educational Technology and Applied Learning Science (METALS) program, which trains graduate students to become learning engineers and learning experience designers.

PDF | In this paper, we compare two artificial neural networks (ANNs) approaches designed to perform channel equalization for millimeter-wave (mmWave)... | Find, read and cite all the research you need on ResearchGate

Cryptocurrency is gaining more ground each year, which means the space demands an even higher level of understanding for anyone who wants to actually come out ahead. What was once a niche interest for very specific groups of investors will soon be accepted by MasterCard and Tesla, while PayPal started integrating the currency late last year. On top of that, crypto trading is surging in popularity as well, emerging as a niche stock market for people who want to experiment with investing from the comfort of their laptop. If you're new to the world of investing, or if you're a seasoned investor worried about losing your edge, The Quantitative Crypto Trading Strategies Bundle is definitely worth a look at $145. It offers intermediate to advanced training on every aspect of cryptocurrency training, from programming and sorting out risks to the implementation of long-term strategies.

With the success of AI/ML in areas like image recognition, medicine, and the web, why are we only seeing incremental gains in the supply chain? What's throttling AI's ability to drive optimal supply chain performance? Ranjit Notani, co-founder and CTO of One Network Enterprises, and Joe Bellini (COO), will explode common myths about AI/ML and why network strategies are essential to success in Supply Chain Control Towers. There's a new approach to automated decision-making and decision-support in the supply chain, that marries network learnings with the unique insights of users -- for a multiparty multi-tier process that creates optimal outcomes. This webinar will help you understand: • The limits of enterprise-centric technology and why you need a network-based control tower strategy • The 5 areas in the supply chain where you can and should use network AI/ML • The 7 Challenges in achieving the full benefit of AI in Supply Chain and how to overcome them • How guardrails and time horizons affect the results you can obtain with AI • How workbenches with predictive and prescriptive analytics streamline problem-solving • Why improving forecasts is not a panacea for your supply chain.

In an effort to increase standardization across the PyTorch ecosystem Facebook AI in a recent blog post told that they would be leveraging Facebook's open-source Hydra framework to handle configs, and also offer an integration with PyTorch Lightning. This post is about Hydra. If you are reading this post then I assume you are familiar with what are config files, why are they useful, and how they increase reproducibility. And you also know what a nightmare is argparse. In general, with config files you can pass all the hyperparameters to your model, you can define all the global constants, define dataset splits, and … without touching the core code of your project.

A great technological shift is on the verge of occurring very soon. Disruptive Artificial Intelligence technologies are going to change the world and human labor will be replaced by robot workers and the shift has in-fact started. This mind-blowing course introduces you to the concept of Artificial Intelligence usage in the workplace along with providing you practical examples of the different platforms that deploy the same for automation. You will learn about the numerous Human Resources tools and usage of these in Artificial Intelligence, along with sales-based AI tools that can help you close the deal. You will be also introduced to Virtual chatbots that look like humans and do all the automation and support work for you in any industry you are in.

This is my first data analysis related video. In this video, I have solved the Fake news detection problem using four machine learning classification algorithms. Hi everyone, This is my first data analysis related video. In this video, I have solved the Fake news detection problem using four machine learning classification algorithms. From this video, you will learn how you can apply Linear regression, Decision Tree classification, Gradient boost classification, and random forest classification model.

Data science is an interdisciplinary field that uses scientific methods, processes, and algorithms to extract knowledge and insights from data. The field of data science has several subdivisions such as data mining, data transformation, data visualization, machine learning, deep learning, etc. This is the stage where data is collected, analyzed to unravel patterns and relationships in the data, and questions are asked to be answered using the data. This is where mathematical skills come into play. In this stage, mathematical tools are used to build models (predictive models) for quantifying patterns or studying the relationships between features in the dataset.

Inductive inference in supervised classification context constitutes to methods and approaches to assign some objects or items into different predefined classes using a formal rule that is derived from training data and possibly some additional auxiliary information. The optimality of such an assignment varies under different conditions due to intrinsic attributes of the objects being considered for such a task. One of these cases is when all the objects' features are discrete variables with a priori known categories. As another example, one can consider a modification of this case with a priori unknown categories. These two cases are the main focus of this thesis and based on Bayesian inductive theories, de Finetti type exchangeability is a suitable assumption that facilitates the derivation of classifiers in the former scenario. On the contrary, this type of exchangeability is not applicable in the latter case, instead, it is possible to utilise the partition exchangeability due to John Kingman. These two types of exchangeabilities are discussed and furthermore here I investigate inductive supervised classifiers based on both types of exchangeabilities. I further demonstrate that the classifiers based on de Finetti type exchangeability can optimally handle test items independently of each other in the presence of infinite amounts of training data while on the other hand, classifiers based on partition exchangeability still continue to benefit from joint labelling of all the test items. Additionally, it is shown that the inductive learning process for the simultaneous classifier saturates when the amount of test data tends to infinity.

Learn Machine Learning Practically By Building Projects What you'll learn Description Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to self-learn and improve over time without being explicitly programmed. In short, machine learning algorithms are able to detect and learn from patterns in data and make their own predictions. In traditional programming, someone writes a series of instructions so that a computer can transform input data into a desired output. Instructions are mostly based on an IF-THEN structure: when certain conditions are met, the program executes a specific action. Machine learning, on the other hand, is an automated process that enables machines to solve problems and take actions based on past observations.