If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Created by Jitesh Khurkhuriya 2.5 hours on-demand video course Understood the importance of mathematics to truly understand and learn Data Science and Machine Learning. In this course, we will cover right from the foundations of Algebraic Equations, Linear Algebra, Calculus including Gradient using Single and Double order derivatives, Vectors, Matrices, Probability and much more. Without maths, there is no Machine Learning. Machine Learning uses mathematical implementation of the algorithms and without understanding the math behind it is like driving a car without knowing what kind of engine powers it.
If you want to create convincing output for some purpose, something that isn't real but seems to be, how do you do it? Generative Adversarial Networks (GANs) are an approach which holds a lot of promise. They provide a form of unsupervised learning, where a system can improve its performance over time without human feedback. Imagine that you're an artist hoping to get rich by creating fake "previously undiscovered" Rembrandts. Your works have to be good enough to fool the experts.
IT Operations professionals have always dreamed of being able to predict incidents, i.e., service interruptions and outages that negatively impact the business. Those dreams began to convert to serious hopes and even demands around 2010 with the emergence of commercially viable big data and analytics solutions. The idea here was that, with sufficient quantities of data at hand, and sufficiently powerful statistical analysis tools, deep correlational patterns would emerge from the data. Nirvana was that future events could be predicted with a reasonably high degree of accuracy. Put another way: IT Operations professionals - like many business professionals - were convinced that if one had enough information about the past, the future could be revealed.
Probability and statistics are the basis of Data Science. Statistics is, in simple terms, the use of mathematics to perform technical analysis of data. With the help of statistical methods, we make estimates for further analysis. Statistical methods themselves are dependent on the theory of probability, which allows us to make predictions. Both statistics and probability are separate and complicated fields of mathematics.
We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. We invite you to sign up here to not miss these free books. This book is intended for busy professionals working with data of any kind: engineers, BI analysts, statisticians, operations research, AI and machine learning professionals, economists, data scientists, biologists, and quants, ranging from beginners to executives. In about 300 pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new model-free, data-driven foundations to statistical science and predictive analytics. The approach focuses on robust techniques; it is bottom-up (from applications to theory), in contrast to the traditional top-down approach.
This tutorial is part of the Machine learning for developers learning path. In this tutorial, we describe the basics of solving a classification-based machine learning problem, and give you a comparative study of some of the current most popular algorithms. In the open Notebook, click Run to run the cells one at a time. The rest of the tutorial follows the order of the Notebook. Classification is when the feature to be predicted contains categories of values.
Memento broke many of the traditional paradigms in the film industry by structuring two parallel narratives, one chronologically going backwards and one going forward. The novel form narrative implemented in Memento forces the audience to constantly reevaluate their knowledge of the plot and they keep learning small details every few minutes of the film. It turns out that replaying a knowledge sequence backwards for small time intervals is an incredibly captivating method of learning. Intuitively, the Memento form of learning seems like perfect for AI agents. Last year, researchers from OpenAI leveraged that learning methodology to created AI agents that learned to play Montezuma's Revenge using a single demonstration.
Probabilistic planning captures the uncertainty of plan execution by probabilistically modeling the effects of actions in the environment, and therefore the probability of reaching different states from a given state and action. In order to compute a solution for a probabilistic planning problem, planners need to manage the uncertainty associated with the different paths from the initial state to a goal state. Several approaches to manage uncertainty were proposed, e.g., consider all paths at once, perform determinization of actions, and sampling. In this paper, we introduce trajectory-based short-sighted Stochastic Shortest Path Problems (SSPs), a novel approach to manage uncertainty for probabilistic planning problems in which states reachable with low probability are substituted by artificial goals that heuristically estimate their cost to reach a goal state. We also extend the theoretical results of Short-Sighted Probabilistic Planner (SSiPP) [ref] by proving that SSiPP always finishes and is asymptotically optimal under sufficient conditions on the structure of short-sighted SSPs.
In this article, we have explored how we can classify text into different categories using Naive Bayes classifier. We have used the News20 dataset and developed the demo in Python. As the name suggests, classifying texts can be referred as text classification. Usually, we classify them for ease of access and understanding. We don't need human labour to make them sit all day reading texts and labelling categories.
We describe an approach to speed-up inference with latent variable PCFGs, which have been shown to be highly effective for natural language parsing. Our approach is based on a tensor formulation recently introduced for spectral estimation of latent-variable PCFGs coupled with a tensor decomposition algorithm well-known in the multilinear algebra literature. We also describe an error bound for this approximation, which bounds the difference between the probabilities calculated by the algorithm and the true probabilities that the approximated model gives. Empirical evaluation on real-world natural language parsing data demonstrates a significant speed-up at minimal cost for parsing performance. Papers published at the Neural Information Processing Systems Conference.