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) …
Tali Soroker is a Financial Analyst at I Know First. Differences in the concepts of randomness and chaos are crucial in our abilities to make predictions about a system with such properties. A random system is unpredictable, as a given outcome does not rely on any previous event. A coin that is tossed seven times in a row, landing on heads each time, can be tossed an eighth time and the probability that it will land on heads again is still only 50%. Such stationary processes do not have a change in statistical properties over time and, therefore, cannot be predicted.
This cheatsheet is currently a 9-page reference in basic data science that covers basic concepts in probability, statistics, statistical learning, machine learning, big data frameworks and SQL. The cheatsheet is loosely based off of The Data Science Design Manual by Steven S. Skiena and An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. Inspired by William Chen's The Only Probability Cheatsheet You'll Ever Need, located here. Feel free to suggest comments, updates, and potential improvements!
In 2018 there were so many discussions about Ai, and its infusion into the world around us, that it all blurred into one big online narrative on the topic. We started to accept that Data was the driving force behind huge technical changes, and challenges, and we also began to normalise references to Ai as if it was just the next logical advancement of the internet, and app economies. But let's leave that all behind. I believe that 2019 is the year that Ai is well and truly'here'. This is the year that the conflict and negotiation of this technology will legitimately change the way we comprehend what it means to be human in the modern world.
The Naive Bayes Classifier is a well known machine learning classifier with applications in Natural Language Processing (NLP) and other areas. Despite its simplicity, it is able to achieve above average performance in different tasks like sentiment analysis. Today we will elaborate on the core principles of this model and then implement it in Python. In the end, we will see how well we do on a dataset of 2000 movie reviews. The math behind this model isn't particularly difficult to understand if you are familiar with some of the math notation.
In Dynamic Programming (DP) we have seen that in order to compute the value function on each state, we need to know the transition matrix as well as the reward system. But this is not always a realistic condition. Probably it is possible to have such thing in some board games, but in video games and real life problems like self-driving car there is no way to know these information before hand. If you recall the formula of the State-Value function from "Math Behind Reinforcement Learning" article: It is not possible to compute the V(s) because p(s',r s,a) is now unknown to us. Always keep in mind that our goal is to find the policy that maximizes the reward for an agent.
Some time ago, I spent several evenings playing around with state of the art object detection model called YOLO, which is certainly known to those who are interested in Machine Learning on a daily basis. Originally written in Darknet -- open source neural network framework -- YOLO performs really well in the tasks of locating and recognizing objects on the pictures. Due to the fact that I have been interested in TensorFlow.js for a few weeks now, I decided to check how YOLO will handle the limitations of In-Browser computing. If you want to play with the demo version, visit "I Learn Machne Learning" project website. A few months ago, the third version of YOLO was released.
In recent times, a large part of the airline industry's success has been due to an ability that many industries have struggled with – Optimizing revenue using Artificial Intelligence. Airline ticket prices are decided by algorithms that change fares depending upon several factors such as past bookings, remaining capacity, average demand per routes and the probability of selling more seats later etc.; all of which can be included in a strategy called "Airline Revenue Management". Sabre the largest Global Distribution Systems provider for air bookings in North America describes this as a system that is "used to determine the optimal price of selling a seat at any given point in time". Airline revenue optimization, though, is becoming increasingly difficult citing several factors, not the least of which is the upward trend in candidates to the cheap carrier segment and the ever-increasing list of competitive in-flight services. Plus, with increasing competition in the industry and market volatility, the airline industry is looking for solutions that will offer ways to maximize profits and deliver better customer experience and customer service.
Imagine you are a company selling a fast-moving consumer good in the market. Let's assume that the customer would follow the given journey to make the final purchase: These are the states at which the customer would be at any point in the purchase journey. Now, how to find out in which state the customers would be after 6 months? Markov Chain comes to the rescue!! Let's first understand what Markov Chain is. Let's delve a little deeper.