Interior design involves the careful selection and arrangement of objects to create an aesthetically pleasing, functional, and harmonized space that aligns with the client's design brief. This task is particularly challenging, as a successful design must not only incorporate all the necessary objects in a cohesive style, but also ensure they are arranged in a way that maximizes accessibility, while adhering to a variety of affordability and usage considerations. Data-driven solutions have been proposed, but these are typically room- or domain-specific and lack explainability in their design design considerations used in producing the final layout. In this paper, we investigate if large language models (LLMs) can be directly utilized for interior design. While we find that LLMs are not yet capable of generating complete layouts, they can be effectively leveraged in a structured manner, inspired by the workflow of interior designers. By systematically probing LLMs, we can reliably generate a list of objects along with relevant constraints that guide their placement. We translate this information into a design layout graph, which is then solved using an off-the-shelf constrained optimization setup to generate the final layouts. We benchmark our algorithm in various design configurations against existing LLM-based methods and human designs, and evaluate the results using a variety of quantitative and qualitative metrics along with user studies. In summary, we demonstrate that LLMs, when used in a structured manner, can effectively generate diverse high-quality layouts, making them a viable solution for creating large-scale virtual scenes. Project webpage at https://flairgpt.github.io/

Although there are other variants of cost function as mentioned at the very beginning by saying different variations (see MAE, RMSE, MSE), in this article we will consider the squared error function, which is one of the cost calculation functions and also is effective to use for many regression problems. Since the aim is to find the most accurate model, our main goal is to minimize the cost function, that is, the error. As seen in this image, we should use the optimal theta values of the J cost function, which are the theta values of the point where the error is minimum, in the model. To show it correctly in 2D, let's consider the function simplified, that is, theta zero value (constant) is 0. As can be seen in the figure, we start the calculation by accepting (randomly) the theta 1 value as 0.5. When we calculate the error, we get the value of approximately 0.58 and so, mark the point (0.5, 0.58) on the graph.

Understanding the concept of the gradient is useful for understanding the logic of the gradient descent algorithm. Let's take a look at the explanation of the concept of stationary point in Wikipedia. As it can be understood from here, the gradient descent algorithm takes the points in the cost function and continues with the aim of reducing the derivative (slope) of these points in each iteration. The reason for this is to find the value whose slope is zero, in other words, the minimum point. When the coordinate values of this point are substituted in the hypothesis function, the function we obtain becomes the hypothesis function of the model with the least error we can create.

This paper investigates different methods to detect obstacles ahead of a robot using a camera in the robot, an aerial camera, and an ultrasound sensor. We also explored various efficient path finding methods for the robot to navigate to the target source. Single and multi-iteration angle-based navigation algorithms were developed. The theta-based path finding algorithms were compared with the Dijkstra Algorithm and their performance were analyzed.

In the current tech landscape, developers are expected to have a number of different skills. And many of them do. There are also a lot of different career paths available to developers that use many of their current skills with a slight twist. Database administrators, developer advocates, and machine learning engineers all have one thing in common with all developers: they all know how to code. It doesn't matter which languages are being used, they all understand the core concepts behind writing good code. That's one of the reasons many software developers consider becoming machine learning engineers. With all of the tools and packages available, you don't need to have a deep mathematical background to get accurate results. If you are willing to learn how to use some libraries and get a high-level understanding of the underlying math, you can become a machine learning engineer.

When I started my machine learning journey, math was something that always intrigued me and still does. I for one believe that libraries such as scikit learn have indeed done wonders for us when it comes to implementing the algorithms but without an understanding of the maths that goes into making the algorithm, we are bound to make mistakes on complicated problems. In this article, I will be going over the math behind Gradient Descent and the derivation behind the Normal linear Equation and then implementing them both on a dataset to get my coefficients. When i was getting started with Linear Regression and trying to get an understanding of the different ways to calculate the coefficients, The Normal Equation was by far my most favorite method to find coefficients but where does this equation come from? Well, let us take a look.

Logistic regression is a popular method since the last century. It establishes the relationship between a categorical variable and one or more independent variables. This relationship is used in machine learning to predict the outcome of a categorical variable. It is widely used in many different fields such as the medical field, trading and business, technology, and many more. This article explains the process of developing a binary classification algorithm and implements it on a medical dataset.

Learn to develop a multivariate linear regression for any number of variables in Python from scratch. Linear regression is probably the most simple machine learning algorithm. It is very good for starters because it uses simple formulas. So, it is good for learning machine-learning concepts. In this article, I will try to explain the multivariate linear regression step by step.

This article explains the reason for the poor performance of a machine learning algorithm and how to improve it. If the machine learning algorithm does not work as well as you expected, almost all the time it happens because of bias or variance. The algorithm may be suffering from either underfitting or overfitting or a bit of both. If the machine learning algorithm does not work as well as you expected, almost all the time it happens because of bias or variance. The algorithm may be suffering from either underfitting or overfitting or a bit of both.

Logistic regression predicts the probability of the outcome being true. In this exercise, we will implement a logistic regression and apply it to two different data sets. To learn the basics of Logistic Regression in R read this post. In the first part of this exercise, we will build a logistic regression model to predict whether a student gets admitted into a university. Suppose that you are the administrator of a university department and you want to determine each applicant's chance of admission based on their results on two exams.