Who does not know Python? Mostly used in Data Science and Machine Learning. Let us discuss more about it! When you first learn a program, you are seemingly using a technique called procedural programming. A procedural program is typically a list of instructions that execute one after the other starting from the top of the line. On the other hand, object-oriented programs are built around well objects.

According to the Stack Overflow Developer survey report, Kotlin is one of the most loved programming languages among professional developers. It secured the 4th position among 25 programming languages. As per the official documentation, Kotlin claims to be a preferred choice for building data pipelines, productionising machine learning models, among others. In this article, we list down the top 8 machine learning libraries in Kotlin. Kotlin Statistics is a machine learning library that aims to express meaningful statistical and data analysis with functional and object-oriented programming while making the code legible and intuitive.

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TL;DR scikit-learn does not allow you to add hard-coded rules to your machine learning model, but for many use cases, you should! This article explores how you can leverage domain knowledge and object-oriented programming (OOP) to build hybrid rule-based machine learning models on top of scikit-learn. Supervised machine learning models are great for making predictions under uncertainty; they pick up patterns in past data and accurately extrapolate them into the future. Machine learning has pushed the frontier in fields where determining the most likely outcome, whether a class or specific value, has historically been challenging, prone to error, or too time-consuming or expensive at scale. Still, there exist many domains in which some of all possible outcomes are not ambiguous but certain by definition.

In the world of tech and programming both JavaScript and Python languages are becoming popular day by day. To start a programming career its sometimes difficult to choose a startup language. Some developers prefer JavaScript for the starters and some prefer Python. Both JavaScript and Python are object-oriented languages. In this article we will compare both programming languages and talk about some key differences between JavaScript and Python.

This clearly structured and easy to read textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms supported by motivating examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use. The text assumes some basic experience in computer programming and familiarity in an object-oriented language, but not necessarily with Python. Students of computer science will find this clear and concise textbook to be invaluable for undergraduate courses on data structures and algorithms, at both introductory and advanced levels.

Blog - New features of Python 3.8 Sat, 30 Nov 2019 In this blog, I show how to use new features of Python 3.8. Video Tutorial - Upcasting and Downcasting in Java Wed, 27 Nov 2019 In this video, I explain upcasting and downcasting in Java Video Tutorial - How to use Lamdba Expressions in Java Sun, 24 Nov 2019 In this video, I demonstrate how to use Lambda Expression, Lambda Blocks, and Method Reference in Java 8 and above. Video Tutorial - Top-N Analysis in Oracle Database Tue, 19 Nov 2019 In this video, I show how to perform Top-N Analysis in Oracle Database 11g and 18c. Video Tutorial - Why to override equals(), hashCode() and toString() methods of Object class in Java. Sat, 16 Nov 2019 In this video, I explain why to overriding equals(), hashCode() and toString() methods of Object class in Java.

The use of artificial intelligence (AI) is growing at an exponential rate. Businesses are using AI to leverage benefits such as lower costs, increased productivity, and reduced manual errors. Those benefits are so palpable that today, 30% of all companies worldwide are using AI for at least one of their sales processes. But it's also natural to ask yourself which language you should choose for programming AI algorithms. After a little digging, you'll surely find that Python and JavaScript are two top contenders.

Human beings learn causal models and constantly use them to transfer knowledge between similar environments. We use this intuition to design a transfer-learning framework using object-oriented representations to learn the causal relationships between objects. A learned causal dynamics model can be used to transfer between variants of an environment with exchangeable perceptual features among objects but with the same underlying causal dynamics. We adapt continuous optimization for structure learning techniques to explicitly learn the cause and effects of the actions in an interactive environment and transfer to the target domain by categorization of the objects based on causal knowledge. We demonstrate the advantages of our approach in a gridworld setting by combining causal model-based approach with model-free approach in reinforcement learning.

The definition of symbolic descriptions that consistently represent relevant geometrical aspects in manipulation tasks is a challenging problem that has received little attention in the robotic community. This definition is usually done from an observer perspective of a finite set of object relations and orientations that only satisfy geometrical constraints to execute experiments in laboratory conditions. This restricts the possible changes with manipulation actions in the object configuration space to those compatible with that particular external reference definitions, which greatly limits the spectrum of possible manipulations. To tackle these limitations we propose an object-centered representation that permits characterizing a much wider set of possible changes in configuration spaces than the traditional observer perspective counterpart. Based on this representation, we define universal planning operators for picking and placing actions that permits generating plans with geometric and force consistency in manipulation tasks. This object-centered description is directly obtained from the poses and bounding boxes of objects using a novel learning mechanisms that permits generating signal-symbols relations without the need of handcrafting these relations for each particular scenario.