As methodologies for machine learning become more widely used, it is crucial for the creators of machine learning applications to understand what the underlying algorithms are learning and, more importantly, how the different algorithms are deriving patterns from the original information in order to maximize their efficiency. The target audience for this book is data scientists and analysts who are curious about the inner workings of different machine learning algorithms. The knowledge and abilities you get from this book will help you construct the most important predictive models for machine learning and evaluate models that are given to you. This book considers an AI & ML book which is one of the General books. We first develop the algorithms in Excel so that we may take a peep inside the procedures' mysterious black box in order to understand what the machine learning algorithms are learning and how they are learning it.

People Can Fly is one of the leading independent AAA games development studios with an international team of hundreds of talented individuals working from offices located in Poland, UK, US, and Canada, and from all over the world thanks to our remote work programs. Founded in 2002, we made our mark on the shooter genre with titles such as Painkiller, Bulletstorm, Gears of War: Judgment, and Outriders. We are one of the most experienced Unreal Engine studios in the industry and we are expanding it with in-house solutions called PCF Framework. Our creative teams are currently working on several exciting titles: Gemini is our new project being developed with Square Enix; Bifrost, Victoria and Dagger are projects we're growing in the self-publishing model. We also have one project in the concept phase – Red; as well as two projects in VR technology – Green Hell VR and Thunder - a new project based on one of the IPs from the Group's portfolio.

GPT has become a very popular topic in recent times and is being used in many different ways, from automated customer service to natural language processing. This tutorial will show you how to create a GPT-powered chatbot for the Viber app, using the WordPress and no-code plugin Convoworks WP. In it, we'll explain how to set up the chatbot so that you can use GPT-3's natural language technology to ask questions and converse about any topic. To begin setting up your GPT powered chatbot for Viber, you will need to have a WordPress installation that is publicly accessible so it can receive requests from the Viber app. Then, navigate to the Plugin Installer and install and activate Convoworks WP.

In this Vue tutorial, we learn about How to Use Loss Functions in TensorFlow. The loss metric is very important for neural networks. As all machine learning models are one optimization problem or another, the loss is the objective function to minimize. In neural networks, the optimization is done with gradient descent and backpropagation. But what are loss functions, and how are they affecting your neural networks?

Physics simulation is ubiquitous in robotics. Whether in model-based approaches (e.g., trajectory optimization), or model-free algorithms (e.g., reinforcement learning), physics simulators are a central component of modern control pipelines in robotics. Over the past decades, several robotic simulators have been developed, each with dedicated contact modeling assumptions and algorithmic solutions. In this article, we survey the main contact models and the associated numerical methods commonly used in robotics for simulating advanced robot motions involving contact interactions. In particular, we recall the physical laws underlying contacts and friction (i.e., Signorini condition, Coulomb's law, and the maximum dissipation principle), and how they are transcribed in current simulators. For each physics engine, we expose their inherent physical relaxations along with their limitations due to the numerical techniques employed. Based on our study, we propose theoretically grounded quantitative criteria on which we build benchmarks assessing both the physical and computational aspects of simulation. We support our work with an open-source and efficient C++ implementation of the existing algorithmic variations. Our results demonstrate that some approximations or algorithms commonly used in robotics can severely widen the reality gap and impact target applications. We hope this work will help motivate the development of new contact models, contact solvers, and robotic simulators in general, at the root of recent progress in motion generation in robotics.

Resource limitations make it hard to provide all students with one of the most effective educational interventions: personalized instruction. Reinforcement learning could be a key tool to reduce the development cost and improve the effectiveness of intelligent tutoring software that aims to provide the right support, at the right time, to a student. Here we illustrate that deep reinforcement learning can be used to provide adaptive pedagogical support to students learning about the concept of volume in a narrative storyline software. Using explainable artificial intelligence tools, we extracted interpretable insights about the pedagogical policy learned and demonstrated that the resulting policy had similar performance in a different student population. Most importantly, in both studies, the reinforcement-learning narrative system had the largest benefit for those students with the lowest initial pretest scores, suggesting the opportunity for AI to adapt and provide support for those most in need.

Controllable text generation (CTG) by large language models has a huge potential to transform education for teachers and students alike. Specifically, high quality and diverse question generation can dramatically reduce the load on teachers and improve the quality of their educational content. Recent work in this domain has made progress with generation, but fails to show that real teachers judge the generated questions as sufficiently useful for the classroom setting; or if instead the questions have errors and/or pedagogically unhelpful content. We conduct a human evaluation with teachers to assess the quality and usefulness of outputs from combining CTG and question taxonomies (Bloom's and a difficulty taxonomy). The results demonstrate that the questions generated are high quality and sufficiently useful, showing their promise for widespread use in the classroom setting.

Biochemical reaction networks are an amalgamation of reactions where each reaction represents the interaction of different species. Generally, these networks exhibit a multi-scale behavior caused by the high variability in reaction rates and abundances of species. The so-called jump-diffusion approximation is a valuable tool in the modeling of such systems. The approximation is constructed by partitioning the reaction network into a fast and slow subgroup of fast and slow reactions, respectively. This enables the modeling of the dynamics using a Langevin equation for the fast group, while a Markov jump process model is kept for the dynamics of the slow group. Most often biochemical processes are poorly characterized in terms of parameters and population states. As a result of this, methods for estimating hidden quantities are of significant interest. In this paper, we develop a tractable Bayesian inference algorithm based on Markov chain Monte Carlo. The presented blocked Gibbs particle smoothing algorithm utilizes a sequential Monte Carlo method to estimate the latent states and performs distinct Gibbs steps for the parameters of a biochemical reaction network, by exploiting a jump-diffusion approximation model. The presented blocked Gibbs sampler is based on the two distinct steps of state inference and parameter inference. We estimate states via a continuous-time forward-filtering backward-smoothing procedure in the state inference step. By utilizing bootstrap particle filtering within a backward-smoothing procedure, we sample a smoothing trajectory. For estimating the hidden parameters, we utilize a separate Markov chain Monte Carlo sampler within the Gibbs sampler that uses the path-wise continuous-time representation of the reaction counters. Finally, the algorithm is numerically evaluated for a partially observed multi-scale birth-death process example.

The use of Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs) has seen significant growth in the research community, industry, and society. Many of these agents are equipped with communication systems that are essential for completing certain tasks successfully. This has led to the emergence of a new interdisciplinary field at the intersection of robotics and communications, which has been further driven by the integration of UAVs into 5G and 6G communication networks. However, one of the main challenges in this research area is how many researchers tend to oversimplify either the robotics or the communications aspects, hindering the full potential of this new interdisciplinary field. In this paper, we present some of the necessary modeling tools for addressing these problems from both a robotics and communications perspective, using the UAV communications relay as an example.

Are you a problem solver looking for a hands-on internship position with a market-leading company that will help develop your career and reward you intellectually and professionally? Analog Devices, Inc. (NASDAQ: ADI) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, and software technologies into solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world. With revenue of more than $12 billion in FY22 and approximately 25,000 people globally working alongside 125,000 global customers, ADI ensures today's innovators stay Ahead of What's Possible. At ADI, you will learn from the brightest minds who are here to help you grow and succeed.