In today's newsletter, we'll cover a range of topics. You will learn about Data visualization tools, feature engineering, Hadoop tech companies, Data Science History, AI for software dev, Austral language, New deep learning language, DevOps and career scope of Data analyst and other useful tools. We hope you enjoy it! Data visualizations are everywhere today. From creating a visual representation of data points to impress potential investors, report on progress, or even visualize concepts for customer segments, data visualizations are a valuable tool in a variety of settings.

A new academic program developed at MIT aims to teach U.S. Air and Space Forces personnel to understand and utilize artificial intelligence technologies. In a recent peer-reviewed study, the program researchers found that this approach was effective and well-received by employees with diverse backgrounds and professional roles. The project, which was funded by the Department of the Air Force–MIT Artificial Intelligence Accelerator, seeks to contribute to AI educational research, specifically regarding ways to maximize learning outcomes at scale for people from a variety of educational backgrounds. Experts in MIT Open Learning built a curriculum for three general types of military personnel -- leaders, developers, and users -- utilizing existing MIT educational materials and resources. They also created new, more experimental courses that were targeted at Air and Space Forces leaders.

In January 2022, DLA Piper reported on an announcement of a new initiative, as part of the UK's National AI Strategy, to shape the way organisations and regulators develop technical standards for artificial intelligence ("AI"). The initiative, the AI Standards Hub ("Hub"), was highlighted as a collaborative effort between the Alan Turing Institute, the British Standards Institution, and the National Physical Laboratory, in partnership with the UK Government, to lead the way in developing standards that could be used across all sectors and jurisdictions. On 12 October, in their latest update, the Alan Turing Institute announced that the hard work of the collaborators was finally complete and that the Hub was ready for interaction. While still early in its use, the Hub already contains an array of resources that will allow its users to understand and help shape the role of standards in the development of AI and best practices. The primary goal of the Hub is to advance trustworthy and responsible AI through a focus on standards that can be used as part of governance and innovation tools and mechanisms.

We create a custom data generator class, called NiiDataGenerator, that inherits from the built-in tf.keras.utils.Sequence class. This allows for easy loading of data for training and testing of a deep learning model in batches. The class takes four arguments in the constructor: image_filenames, mask_filenames, batch_size, and image_size. These are the paths to the image files, corresponding mask files, the batch size, and the desired image size, respectively. The class then implements the two required methods of the Sequence class: __len__() and __getitem__().

"A Beginner's Guide to Machine Learning with Python, Keras, and scikit-learn" is likely a guide or tutorial for people new to the field of machine learning, who want to learn how to use the Python programming language, along with the Keras and scikit-learn libraries, to build and train machine learning models. Python is a popular programming language for machine learning because it has a large number of libraries and frameworks that make it easy to implement machine learning algorithms. Keras is a high-level neural network API, written in Python and capable of running on top of TensorFlow (or Theano/CNTK). It is a user-friendly and intuitive framework for building and training neural networks. The guide would likely cover the basic concepts of machine learning, as well as walk the reader through the process of building and training different types of machine learning models using Python, Keras, and scikit-learn.

Sparse datasets are a common problem in machine learning, where many examples have a large number of missing or zero-valued features. This can lead to poor model performance and reduced interpretability of the results. In this article, we will provide a step-by-step guide on how to address the sparsity challenge in datasets, with a focus on real-world application. The first step in resolving the sparsity challenge is to understand why your dataset is sparse in the first place. Sparsity can be caused by the presence of irrelevant features, missing data, or categorical variables with a large number of levels.

The world around us has undergone a radical transformation due to rapid technological advancement in recent decades. The industry of the future generation is evolving, and artificial intelligence is the following change in the making popularly known as Industry 4.0. Indeed, experts predict that artificial intelligence(AI) will be the main force behind the following significant virtual shift in the way we stay, converse, study, live, communicate and conduct business. All facets of our social connection are being transformed by this growing technology. One of the newest areas of educational technology is Artificial Intelligence in the field of Education(AIEd).This study emphasizes the different applications of artificial intelligence in education from both an industrial and academic standpoint. It highlights the most recent contextualized learning novel transformative evaluations and advancements in sophisticated tutoring systems. It analyses the AIEd's ethical component and the influence of the transition on people, particularly students and instructors as well. Finally, this article touches on AIEd's potential future research and practices. The goal of this study is to introduce the present-day applications to its intended audience.

Learning effective reinforcement learning (RL) policies to solve real-world complex tasks can be quite challenging without a high-fidelity simulation environment. In most cases, we are only given imperfect simulators with simplified dynamics, which inevitably lead to severe sim-to-real gaps in RL policy learning. The recently emerged field of offline RL provides another possibility to learn policies directly from pre-collected historical data. However, to achieve reasonable performance, existing offline RL algorithms need impractically large offline data with sufficient state-action space coverage for training. This brings up a new question: is it possible to combine learning from limited real data in offline RL and unrestricted exploration through imperfect simulators in online RL to address the drawbacks of both approaches? In this study, we propose the Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning (H2O) framework to provide an affirmative answer to this question. H2O introduces a dynamics-aware policy evaluation scheme, which adaptively penalizes the Q function learning on simulated state-action pairs with large dynamics gaps, while also simultaneously allowing learning from a fixed real-world dataset. Through extensive simulation and real-world tasks, as well as theoretical analysis, we demonstrate the superior performance of H2O against other cross-domain online and offline RL algorithms. H2O provides a brand new hybrid offline-and-online RL paradigm, which can potentially shed light on future RL algorithm design for solving practical real-world tasks.

Artificial intelligence (AI), in particular Machine Learning (ML), promises great advances for digitization, modeling, and optimization of chemical industrial processes. We are seeking a highly motivated Postdoc to work on the combination of process optimization (i.e., Process Systems Engineering (PSE)) and the ML domain. In this postdoc project, you will develop novel tools for the optimization of chemical processes. The project is part of a direct collaboration with a leading energy company that provides process design data. You are leading this collaboration and you will possibly co-supervise an associated PhD project.

Created by Alexander Hanneman 1.5 hours on-demand video course You're here, so you've probably heard about ChatGPT and how it's going to change the world. This course is designed for those who are interested in leveraging A.I ChatGPT in their specific domain or niche. Whether you're a marketer, business strategist, finance professional, teacher or student, or a creative artist – this course goes over dozens of examples on how to get the most out of ChatGPT. Even if you're a writer, or a tradesperson – this class can still be of great use to you. The entire world will use this tool within a matter of months.