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.

Please join us for a virtual Google workshop on "Conceptual Understanding of Deep Learning" When: May 17th 9am-4pm PST. Goal: How does the Brain/Mind (perhaps even an artificial one) work at an algorithmic level? While deep learning has produced tremendous technological strides in recent decades, there is an unsettling feeling of a lack of "conceptual" understanding of why it works and to what extent it will work in the current form. The goal of the workshop is to bring together theorists and practitioners to develop an understanding of the right algorithmic view of deep learning, characterizing the class of functions that can be learned, coming up with the right learning architecture that may (provably) learn multiple functions, concepts and remember them over time as humans do, theoretical understanding of language, logic, RL, meta learning and lifelong learning. The speakers and panelists include Turing award winners Geoffrey Hinton, Leslie Valiant, and Godel Prize winner Christos Papadimitriou (full-details).

Lucknow: Applicants wishing to pursue their career in artificial intelligence (AI) or data science will not need to seek a facility outside of Lucknow. The Institute of Engineering Technology (IET) will launch M.Tech in AI and data science from this year. The growing demand for artificial intelligence and data science in health and the pharmaceutical sector, especially during the Covid-19 epidemic, has made these two studies known to students. "We will provide 18 seats in M.Tech AI and Data Science. Also, the institute is scheduled to receive approval from the All India Council for Technical Education (AICTE) for three courses: M.Tech (mechanical engineering), M.Tech (structural), M.Tech (strength and power) which will assist students in international acquisition and international relations," said the director of IET Prof. Vineet Kansal.

Created by Lazy Programmer Team, Lazy Programmer Inc.Preview this Course - GET COUPON CODE Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering? Today, you can stop imagining, and start doing. This course will teach you the core fundamentals of financial engineering, with a machine learning twist. We will cover must-know topics in financial engineering, such as: Exploratory data analysis, significance testing, correlations, alpha and beta Time series analysis, simple moving average, exponentially-weighted moving average Holt-Winters exponential smoothing model Efficient Market Hypothesis Random Walk Hypothesis Time series forecasting ("stock price prediction") Modern portfolio theory Efficient frontier / Markowitz bullet Mean-variance optimization Maximizing the Sharpe ratio Convex optimization with Linear Programming and Quadratic Programming Capital Asset Pricing Model (CAPM) Algorithmic trading (VIP only) Statistical Factor Models (VIP only) Regime Detection with Hidden Markov Models (VIP only) In addition, we will look at various non-traditional techniques which stem purely from the field of machine learning and artificial intelligence, such as: Classification models Unsupervised learning Reinforcement learning and Q-learning ***VIP-only sections (get it while it lasts!) You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense.

Created by Lazy Programmer Inc. English [Auto], Indonesian [Auto], 6 more Students also bought Advanced AI: Deep Reinforcement Learning in Python Artificial Intelligence: Reinforcement Learning in Python Deep Learning: Recurrent Neural Networks in Python Deep Learning Prerequisites: Logistic Regression in Python Deep Learning Prerequisites: Linear Regression in Python Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Preview this Udemy Course GET COUPON CODE Description This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. Neural networks are one of the staples of machine learning, and they are always a top contender in Kaggle contests. If you want to improve your skills with neural networks and deep learning, this is the course for you. You already learned about backpropagation, but there were a lot of unanswered questions.

We present an approach for the runtime verification of classification systems via data abstraction. Data abstraction relies on the notion of box with a resolution. Boxbased abstraction consists in representing a set of values by its minimal and maximal values in each dimension. We augment boxes with a notion of resolution; this allows to define the notion of clustering coverage, which is intuitively a quantitative metric over boxes that indicates the quality of the abstraction. This allows studying the effect of different clustering parameters on the constructed boxes and estimating an interval of sub-optimal parameters. Moreover, we show how to automatically construct monitors that make use of both the correct and incorrect behaviors of a classification system. This allows checking the size of the monitor abstractions and analysing the separability of the network. Monitors are obtained by combining the sub-monitors of each class of the system placed at some selected layers. Our experiments demonstrate the effectiveness of our clustering coverage estimation and show how to assess the effectiveness and precision of monitors according to the selected clustering parameter and the chosen monitored layers.

It's hard to ignore the cultural and organizational impact that Artificial Intelligence (AI) has had over us. Most organizations today have realized the impact of AI, and are doing all that they can to participate in and help facilitate the growth of the technology. For those who know the nuances of AI and the metrics involved in it, Deep Learning and Machine Learning may not look like challenging terms. But, for those who are new to AI, these terms might be hard to understand. To understand the complications organizations face when adopting machine learning, we must first fully understand the difference between deep learning and machine learning.

Description: Deep learning is a cutting-edge form of machine learning inspired by the architecture of the human brain, but it doesn't have to be intimidating. With TensorFlow, coupled with the Keras API and Python, it's easy to train, test, and tune deep learning models without knowing advanced math. To start this Skill Path, sign up for Codecademy Pro. Description: Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition, and artificial intelligence, including the famous AlphaGo. In this course, you'll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python.

Please note this course has now been updated to Unity 2020.1. This is the long-awaited sequel to the Complete Unity Developer - one of the most popular e-learning courses on the internet! You will benefit from the fact we have already taught over 700,000 students programming and game development, many shipping commercial games as a result. Unity is an incredible 3D package used for making video games, architectural and medical imaging and more. The challenge is that it's big and complicated to use, especially for complete beginners to coding and game development.

Learn how to use the R programming language for data science and machine learning and data visualization! Hello everyone and welcome to the lecture on histograms and this lecture we're going to learn how to create histograms with our. We're going to first start off with installing Gigia plot to will also install a dataset with related the Gilia plot to called a cheesy plot to movies dataset. And before we actually start coding anything I'm going to show you a great cheat sheet resource that our studio provides for you for work and of G-G plot 2. OK. I'm super excited to show you all this. So let's go ahead and jump to our studio. OK so here we are our studio. Let's go ahead and start off by installing the packages we need. You're going to need to install G-G plot 2. So the start off will just go to Head and in the console you can say installed packages in quotes.