Text classification describes a general class of problems such as predicting the sentiment of tweets and movie reviews, as well as classifying email as spam or not. Deep learning methods are proving very good at text classification, achieving state-of-the-art results on a suite of standard academic benchmark problems. In this post, you will discover some best practices to consider when developing deep learning models for text classification. Best Practices for Document Classification with Deep Learning Photo by storebukkebruse, some rights reserved. Take my free 7-day email crash course now (with code).

Reducing the number of input variables for a predictive model is referred to as dimensionality reduction. Fewer input variables can result in a simpler predictive model that may have better performance when making predictions on new data. Perhaps the most popular technique for dimensionality reduction in machine learning is Principal Component Analysis, or PCA for short. This is a technique that comes from the field of linear algebra and can be used as a data preparation technique to create a projection of a dataset prior to fitting a model. In this tutorial, you will discover how to use PCA for dimensionality reduction when developing predictive models.

Recent works on shared autonomy and assistive-AI technologies, such as assistive robot teleoperation, seek to model and help human users with limited ability in a fixed task. However, these approaches often fail to account for humans' ability to adapt and eventually learn how to execute a control task themselves. Furthermore, in applications where it may be desirable for a human to intervene, these methods may inhibit their ability to learn how to succeed with full self-control. In this paper, we focus on the problem of assistive teaching of motor control tasks such as parking a car or landing an aircraft. Despite their ubiquitous role in humans' daily activities and occupations, motor tasks are rarely taught in a uniform way due to their high complexity and variance. We propose an AI-assisted teaching algorithm that leverages skill discovery methods from reinforcement learning (RL) to (i) break down any motor control task into teachable skills, (ii) construct novel drill sequences, and (iii) individualize curricula to students with different capabilities. Through an extensive mix of synthetic and user studies on two motor control tasks -- parking a car with a joystick and writing characters from the Balinese alphabet -- we show that assisted teaching with skills improves student performance by around 40% compared to practicing full trajectories without skills, and practicing with individualized drills can result in up to 25% further improvement. Our source code is available at https://github.com/Stanford-ILIAD/teaching

Semantic communication is viewed as a revolutionary paradigm that can potentially transform how we design and operate wireless communication systems. However, despite a recent surge of research activities in this area, the research landscape remains limited. In this tutorial, we present the first rigorous vision of a scalable end-to-end semantic communication network that is founded on novel concepts from artificial intelligence (AI), causal reasoning, and communication theory. We first discuss how the design of semantic communication networks requires a move from data-driven networks towards knowledge-driven ones. Subsequently, we highlight the necessity of creating semantic representations of data that satisfy the key properties of minimalism, generalizability, and efficiency so as to do more with less. We then explain how those representations can form the basis a so-called semantic language. By using semantic representation and languages, we show that the traditional transmitter and receiver now become a teacher and apprentice. Then, we define the concept of reasoning by investigating the fundamentals of causal representation learning and their role in designing semantic communication networks. We demonstrate that reasoning faculties are majorly characterized by the ability to capture causal and associational relationships in datastreams. For such reasoning-driven networks, we propose novel and essential semantic communication metrics that include new "reasoning capacity" measures that could go beyond Shannon's bound to capture the convergence of computing and communication. Finally, we explain how semantic communications can be scaled to large-scale networks (6G and beyond). In a nutshell, we expect this tutorial to provide a comprehensive reference on how to properly build, analyze, and deploy future semantic communication networks.

For a Self-reliant India, to fulfil demand of highly skilled Artificial Intelligence and Cyber Security professionals in the country, Defence Institute of Advanced Technology, DIAT, Pune is conducting the nationwide Online Training and Certification Courses (OTCC) in Cyber Security, Artificial Intelligence & Machine Learning(AI & ML). The School of Computer Engineering and Mathematical Sciences of DIAT has completed two batches of these courses and more than 1600 candidates are successfully trained and certified. The 3rd batch of AI & ML course is on-going. Now DIAT is launching next batches of 16-weeks Online Course on Cyber Security, and 12-weeks Online Course on Artificial Intelligence & Machine Learning (AI & ML)in December 2022. The Graduating students, professionals, or any graduate person can apply for these courses.

Have you ever wanted to build a customized recommender system for yourself? If Yes! Then this is the course you are looking for. Have you ever thought how YouTube adjust your feed as per your favorite content? Why is your Netflix recommending you your favorite TV shows? Have you ever wanted to build a customized recommender system for yourself?

At the end of each week, you'll reflect on your learning and plot. Next steps to apply what you've learned in your professional practice. This is an important part of the course that we hope you'll use as a roadmap to better manage your team's data science projects. Now let's talk about what this course is all about. The aim of the course is to equip executives with the knowledge that will enable them to work productively with data scientists.

Classic pathfinding algorithms like Dijkstra's Algorithm and A* are used to generate travel routes in applications such as video games, mobile robotics, and architectural design. Despite the popularity of these algorithms, the paths they produce rarely go straight. In this article, you'll learn how to compute highly direct paths using a counting technique inspired by Pascal's Triangle. It's an idea my colleagues and I developed and recently published in the Journal of Artificial Intelligence Research [1]. With the simple step of counting paths, you can overcome a long-standing problem with traditional pathfinding.

Another advantage of unsupervised learning is that the candidates can complete the entire course on their own in India. There is no teacher supervision involved. However, the downside is that no assistance is provided by the instructor in case of an accident during training. If the candidate has any query regarding anything then they should directly ask their instructor. For those who are interested in doing unsupervised machine learning courses in India, they need to do a little research in this field.

Machine Learning for Probabilistic Prediction Quantitative Finance Webinar, Stony Brook University (11/11/2022) Valery Manokhin, PhD, MBA, CFQ Speaker Bio • PhD in Machine Learning (2022) from Royal Holloway, University of London • During PhD conducted research and published papers in probabilistic and conformal prediction. PhD supervised by Prof. Vladimir Vovk, the creator of Conformal Prediction (Prof. Vladimir Vovk is the last PhD student of Andrey Kolmogorov) • Dr. Valery Manokhin holds a number of advanced MSc degrees including from the Moscow Institute of Physics and Technology (Physics/Math), UCL (Computational Statistics and Machine Learning), University of Sussex (Quant Finance) and an MBA from the University of Warwick • Published in the leading machine learning journals, including'Neurocomputing', 'Journal of Machine Learning Research' and'Machine Learning Journal', also in the industry journals including'Frontiers in Energy Research' • Created'Awesome Conformal Prediction' - the most comprehensive professionally curated resource on Conformal Prediction (over 900 stars on GitHub). 'Awesome Conformal Prediction' has been featured at the leading conferences such as ICML and in Kevin Murphy's bestselling book'Probabilistic Machine Learning: An Introduction' Outline of this webinar Introduction to Probabilistic Prediction Probability Calibration Introduction to Conformal Prediction Conformal Prediction for Classification Conformal Prediction for Regression Conclusion 3 Why Probabilistic Prediction? Machine Learning is primarily concerned with producing functions mapping objects onto predicted labels Classical statistical techniques - for small scale, low-dimensional data High-dimensional data does not necessarily follow well-known distributions and hence required new approaches (e.g.