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The quest to make an AI that can play competitive Pokémon

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An AI can beat a chess grandmaster. An AI can become the StarCraft esports champion. But creating an AI that could play Pokémon at the competitive level has been a more elusive problem. Thanks to the variety of monsters, stats, moves, and items, a Pokémon battle has hundreds of thousands of factors for any player -- or machine -- to consider. But that hasn't stopped some people from trying.


Improving Minimax performance

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The Minimax algorithm, also known as MinMax, is a popular algorithm for calculating the best possible move a player can player in a zero-sume game, like Tic-Tac-Toe or Chess. It makes use of an evaluation-function provided by the developer to analyze a given game board. During the execution Minimax builds a game tree that might become quite large. This causes a very long runtime for the algorithm. In this article I'd like to introduce 10 methods to improve the performance of the Minimax algorithm and to optimize its runtime.


Difference between distributed learning versus federated learning algorithms - KDnuggets

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Distributed machine learning algorithm is a multi-nodal system that builds training models by independent training on different nodes. Having a distributed training system accelerates training on huge amounts of data. When working with big data, training time exponentially increases which makes scalability and online re-training. For example, let's say we want to build a recommendation model, and based on the user interaction everyday, we wish to re-train the models. We could see the user-interaction as high as hundreds of clicks per user and millions of users.


Graph Neural Networks

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In this part we are going to learn more about graphs concepts then we explain simple example about how to read karate club datasets: after this part we are ready to dig into graph convolution neural networks. The recent success of graph neural networks(GNNs) for analyzing the graphs' domain has attracted more researchers in this field. CNN is a type of deep learning model for processing data that has a sequence or grid pattern(text, images), which is inspired by the visual system of mammals organization and designed to automatically and adaptively multi-scale localized features, from low-to-high-level patterns. CNN is a mathematical framework typically composed of three types of layers ( convolution, pooling, and fully connected layers), and they apply for object detection, speech recognition, and other Euclidean data structures. Deep learning can extract meaningful local features of Euclidean data, such as images.


Book Metadata and Cover Retrieval Using OCR and Google Books API - KDnuggets

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Most of the time, the raw data that we need for our data science project is not organized in a neat, well-structured, and insightful table. Rather, this is sometimes stored as text in a scanned document. Words in the document must then be extracted one by one to form a text formatted data cell. This is the task performed by Optical Character Recognition (OCR). As you read the words of this article, be it text or number, your eyes are able to process them by recognizing light and dark patterns that make up characters (e.g., letters, number, punctuation marks, etc.).


What is an Artificial Neural Network (ANN)?

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Learnbay offers data science certification courses in Bangalore. The term "artificial neural network" (ANN) refers to a hardware or software system in information technology (IT) that mimics the functioning of neurons in the human brain. A class of deep learning technology, ANNs (also known as neural networks) are a subset of AI (artificial intelligence). Solving sophisticated signal processing or pattern recognition challenges is where these technologies find commercial use. Handwriting recognition for check processing, speech-to-text transcription, oil exploration data analysis, weather prediction, and facial recognition are just a few examples of the many commercial uses that have emerged since 2000.


Top 5 techniques for Explainable AI

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As you can see that all these explainable AI techniques are not "nice-to-have", but mandatory. Using these techniques will help you better communicate with the person impacted through AI decisions. In some cases, as seen in the stroke prediction example, understanding these techniques can help improve or save lives. You can experience some of the techniques in this article on my website -- https://experiencedatascience.com


7 things to know before using AWS Panorama

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Machine learning is becoming essential for a lot of companies and they want to use it to optimize their operations and make new services. One of the challenges is that sometimes you need to deploy a model in an environment where you have limited internet connection and no operators to manage the infrastructure for ML. In this case, you need to use Machine Learning on Edge and have a way to deploy and monitor your models and applications remotely. AWS Panorama is a machine learning device by AWS with a software development kit and corresponding AWS service which manages devices and applications. It is focused on working with computer vision models and video streams.


Limitations of Deep Learning

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With ML (Machine Learning) we can already determine what is ahead for us. In terms of google searching based on the previous searches you have made, or your Instagram feed shows you posts related to what you are interested in. The term Deep Learning has been coming up more than ever. Machine learning and Deep learning are two different variants of AI (Artificial Intelligence) while Deep learning is a branch of Machine Learning, but what exactly is Deep Learning? When you unlock your iPhone using FaceID or syncing music on Snapchat to determine which song it is, you are using Deep Learning algorithms without knowing it.


Understanding Machine Learning Algorithms - KDnuggets

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Machine learning is now mainstream. And given the success companies see deriving value from the vast amount of available data, everyone wants in. But while the thought of machine learning can seem overwhelming, it's not magic, and the basic concepts are fairly simple. Here I'll give you a foundation for understanding the ideas behind some of the most popular machine learning algorithms. Devised by Leo Breiman in 2001, a random forest is a simple, yet powerful algorithm comprised of an ensemble (or collection)of independently trained decision trees.