Do your non-player characters lack drive and ambition? Are they slow, stupid and constantly banging their heads against the wall? Then this course is for you. Join Penny as she explains, demonstrates and assists you in creating your very own NPCs in Unity with C#. All you need is a sound knowledge of Unity, C# and the ability to add two numbers together. In this course, Penny reveals the most popular AI techniques used for creating believable character behaviour in games using her internationally acclaimed teaching style and knowledge from over 25 years working with games, graphics and having written two award winning books on games AI.

Running your Python scripts is an important step in the development process, because it is in this manner that you'll get to find out if your code works as you intended it to. It is, also, often the case that we would need to pass information to the Python script for it to function. In this tutorial, you will discover various ways of running and passing information to a Python script. Running and Passing Information to a Python Script Photo by Andrea Leopardi, some rights reserved. The command-line interface is used extensively for running Python code.

I will recommend this class to any one looking towards Data Science" "This course so far is breaking down the content into smart bite-size pieces and the professor explains everything patiently and gives just enough background so that I do not feel lost." "This course is really good for me. it is easy to understand and it covers a wide range of NLP topics from the basics, machine learning to Deep Learning. The codes used is practical and useful. I definitely satisfy with the content and surely recommend to everyone who is interested in Natural Language Processing"

Welcome to Deep Reinforcement Learning 2.0! In this course, we will learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, we are able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field). In this part we will study all the fundamentals of Artificial Intelligence which will allow you to understand and master the AI of this course. These include Q-Learning, Deep Q-Learning, Policy Gradient, Actor-Critic and more.

A state machine model is a mathematical model that groups all possible system occurrences, called states. The course emphasizes project-based learning, learning by doing. The goal of this course is to introduce an event-driven programming paradigm using simple and hierarchical state machines. After going through this course, you will be trained to apply the state machine approach to solve your complex embedded systems projects. If you are a beginner in the field of embedded systems, then you can take our courses in the below-mentioned order.

This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python (V2). The reason I made this course is because there is a huge gap for many students between machine learning "theory" and writing actual code. As I've always said: "If you can't implement it, then you don't understand it". Without basic knowledge of data manipulation, vectors, and matrices, students are not able to put their great ideas into working form, on a computer. This course closes that gap by teaching you all the basic operations you need for implementing machine learning and deep learning algorithms.

Machine learning is a hot issue in this age of artificial intelligence. Nobody could have predicted that computer vision and predictive analytics would break new ground. Face recognition on smartphones, language translation software, and self-driving cars are all examples of how we are increasingly seeing both of them in our daily lives. What may appear to be science fiction is becoming a reality, and it is only a matter of time before we achieve Artificial General Intelligence. Machine Learning Course in Chennai at FITA Academy provides the best practical sessions to develop your skills technically.

Created by Andy Bek 26.5 hours on-demand video course Welcome to the best resource online and the only one you need to learn and master object-oriented programming with python! There has never been a better time to learn python. It is consistently ranked in the top 3 most in-demand and most-loved programming languages in the world, with applications in machine learning, web development, data science, automation, game development, and much more. And its growth shows no signs of stopping. But while there are plenty of resources to learn the basics of python, it is quite difficult to move past those to the intermediate and advanced facets of the language. This course seeks to address that.

Machine learning methods especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for training. In real-world applications, we often need to address sample shortage due to e.g., dynamic contexts with emerging prediction targets and costly sample annotation. Therefore, low-resource learning, which aims to learn robust prediction models with no enough resources (especially training samples), is now being widely investigated. Among all the low-resource learning studies, many prefer to utilize some auxiliary information in the form of Knowledge Graph (KG), which is becoming more and more popular for knowledge representation, to reduce the reliance on labeled samples. In this survey, we very comprehensively reviewed over $90$ papers about KG-aware research for two major low-resource learning settings -- zero-shot learning (ZSL) where new classes for prediction have never appeared in training, and few-shot learning (FSL) where new classes for prediction have only a small number of labeled samples that are available. We first introduced the KGs used in ZSL and FSL studies as well as the existing and potential KG construction solutions, and then systematically categorized and summarized KG-aware ZSL and FSL methods, dividing them into different paradigms such as the mapping-based, the data augmentation, the propagation-based and the optimization-based. We next presented different applications, including not only KG augmented tasks in Computer Vision and Natural Language Processing (e.g., image classification, text classification and knowledge extraction), but also tasks for KG curation (e.g., inductive KG completion), and some typical evaluation resources for each task. We eventually discussed some challenges and future directions on aspects such as new learning and reasoning paradigms, and the construction of high quality KGs.

The topics outlined below are taught using practical Python projects!