Bots are now a key starting point for conversations with customers, so it's vital that companies think through how they use them. Artificial intelligence is a technology that has already transformed how consumers interact with their home devices, with brands, even with their cars. It has shown benefits both for companies and customers, but what's next for virtual agents and their kin? In this webinar, P.V. Kannan, coauthor of "The Future of Customer Service Is AI-Human Collaboration," discusses how virtual agents are proving themselves as a technology and the ways AI-driven customer service will empower contact center agents to provide great customer experiences. Get periodic email updates on upcoming webinars, panel discussions, and other special events.

A hallmark of human intelligence is the ability to construct self-contained chunks of knowledge and adequately reuse them in novel combinations for solving different yet structurally related problems. Learning such compositional structures has been a significant challenge for artificial systems, due to the combinatorial nature of the underlying search problem. To date, research into compositional learning has largely proceeded separately from work on lifelong or continual learning. We integrate these two lines of work to present a general-purpose framework for lifelong learning of compositional structures that can be used for solving a stream of related tasks. Our framework separates the learning process into two broad stages: learning how to best combine existing components in order to assimilate a novel problem, and learning how to adapt the set of existing components to accommodate the new problem. This separation explicitly handles the trade-off between the stability required to remember how to solve earlier tasks and the flexibility required to solve new tasks, as we show empirically in an extensive evaluation.

Data Science and Machine Learning have become fundamental assets for companies and research institutions alike. As one of its fields, supervised classification allows for class prediction of new samples, learning from given training data. However, some properties can cause datasets to be problematic to classify. In order to evaluate a dataset a priori, data complexity metrics have been used extensively. They provide information regarding different intrinsic characteristics of the data, which serve to evaluate classifier compatibility and a course of action that improves performance. However, most complexity metrics focus on just one characteristic of the data, which can be insufficient to properly evaluate the dataset towards the classifiers' performance. In fact, class overlap, a very detrimental feature for the classification process (especially when imbalance among class labels is also present) is hard to assess. This research work focuses on revisiting complexity metrics based on data morphology. In accordance to their nature, the premise is that they provide both good estimates for class overlap, and great correlations with the classification performance. For that purpose, a novel family of metrics have been developed. Being based on ball coverage by classes, they are named after Overlap Number of Balls. Finally, some prospects for the adaptation of the former family of metrics to singular (more complex) problems are discussed.

Machine learning predictive modeling performance is only as good as your data, and your data is only as good as the way you prepare it for modeling. The most common approach to data preparation is to study a dataset and review the expectations of a machine learning algorithms, then carefully choose the most appropriate data preparation techniques to transform the raw data to best meet the expectations of the algorithm. This is slow, expensive, and requires a vast amount of expertise. An alternative approach to data preparation is to grid search a suite of common and commonly useful data preparation techniques to the raw data. This is an alternative philosophy for data preparation that treats data transforms as another hyperparameter of the modeling pipeline to be searched and tuned.

In this tutorial, you will learn how to visualize class activation maps for debugging deep neural networks using an algorithm called Grad-CAM.

Note: If you prefer to read with a white background and black font, you can see this article in GitHub here. Las time I check SVG images rendered just fine. It is no exaggeration to say that NumPy is at the core of the entire scientific computing Python ecosystem, both as a standalone package for numerical computation and as the engine behind most data science packages. In this document, I review NumPy main components and functionality, with attention to the needs of Data Science and Machine Learning practitioners, and people who aspire to become a data professional. My only assumption is that you have basic familiarity with Python, things like variables, lists, tuples, and loops. Advance Python concepts like Object Oriented Programming are not touched at all. Content-wise, I'll say that 95% is based on NumPy v1.18 manual, in particular: The rest 5% comes from a couple of random articles on the Internet and Stack Overflow. I resort to those sources mostly to clarify concepts and functionality that wasn't clear for me from NumPy documentation. My own experience was the base to organize the tutorial, explain concepts, create practical examples, create images, etc. "Why are you using the documentation as the main source of content, instead of the many great tutorials online?" Because it is the most up-to-date, complete, and reliable source about NumPy (and about any library for that matter). "Why then I should read this if everything comes from the documentation?" Well, you don't need to read this, you are right. Actually, I encourage you to read the documentation and learn from there. What I can offer is my own: (1) organization of contents, (2) selection of contents, (3) explanations and framing of concepts, (4) images, (5) practical examples, (6) and general perspective. This tutorial is part of a larger project I am working on, which is an introduction to Python and its libraries for scientific computing, data science, and machine learning that you can find here. As a final note, if you are NumPy expert, advanced user, or developer, you may find some inaccuracies or lack of depth in some of my explanations. Two things: (1) feel free to suggest a better explanation or something that I may add to make things clearer, (2) I prioritize conciseness and accessibility over the accuracy, so the lack of accuracy or depth sometimes it is intentional from my part. If you have any questions or suggestion feel free to reach me out to at pcaceres@wisc.edu Here is my Twitter, LinkedIn, and personal site. Scientific and numerical computing often requires processing massive datasets with complex algorithms. If you are a scientist or data professional, you want a programming language than can process data FAST. The closer a programming language is to machine instructions (binary), the faster it runs.

This Python for Machine Learning Tutorial will help you learn the Python programming language from scratch. You'll learn about Classes and Objects in Python. Everything in this course is explained with the relevant example thus you will actually know how to implement the topics that you will learn in this course.

The Computer Vision and Pattern Recognition conference (CVPR) was held virtually on 14-19 June. As well as invited talks, posters and workshops, there were a number of tutorials on a range of topics. Timnit Gebru and Emily Denton were the organisers of one of the tutorials, which covered fairness, accountability, transparency and ethics in computer vision. As the organisers write in the introduction to their tutorial, computer vision is no longer a purely academic endeavour; computer vision systems have been utilised widely across society. Such systems have been applied to law enforcement, border control, employment and healthcare.

Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial: How to use Keras, a neural network API written in Python and integrated with TensorFlow. We will learn how to prepare and process data for artificial neural networks, build and train artificial neural networks from scratch, build and train convolutional neural networks (CNNs), implement fine-tuning and transfer learning, and more!

This is a Hands-on 1- hour Machine Learning Project using Pyspark. Pyspark is the collaboration of Apache Spark and Python. PySpark is a tool used in Big Data Analytics. Apache Spark is an open-source cluster-computing framework, built around speed, ease of use, and streaming analytics whereas Python is a general-purpose, high-level programming language. It provides a wide range of libraries and is majorly used for Machine Learning and Real-Time Streaming Analytics.