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Artificial Intelligence 2018: Build the Most Powerful AI


Free Coupon Discount - Artificial Intelligence 2018: Build the Most Powerful AI, Learn, build and implement the most powerful AI model at home. Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team Students also bought Artificial Intelligence Masterclass The Complete Neural Networks Bootcamp: Theory, Applications TensorFlow 2.0 Practical Modern Reinforcement Learning: Deep Q Learning in PyTorch Deep Reinforcement Learning 2.0 TensorFlow 2.0 Practical Advanced Preview this Udemy Course GET COUPON CODE Description Two months ago we discovered that a very new kind of AI was invented. The kind of AI which is based on a genius idea and that you can build from scratch and without the need for any framework. We checked that out, we built it, and... the results are absolutely insane! This game-changing AI called Augmented Random Search, ARS for short.

Artificial Intelligence Masterclass


Today, we are bringing you the king of our AI courses...: Are you keen on Artificial Intelligence? Do want to learn to build the most powerful AI model developed so far and even play against it? Then Artificial Intelligence Masterclass course is the right choice for you. This ultimate AI toolbox is all you need to nail it down with ease. You will get 10 hours step by step guide and the full roadmap which will help you build your own Hybrid AI Model from scratch.

Forthcoming machine learning and AI seminars: December 2021 edition


This post contains a list of the AI-related seminars that are scheduled to take place between 8 December 2021 and 31 January 2022. All events detailed here are free and open for anyone to attend virtually. Title to be confirmed Speaker: Casey Fiesler (University of Colorado Boulder) Organised by: New York University To ask to join, send an email to the organisers. Speaker: Fei-Fei Li Organised by: Harvard ML Theory Join the mailing list to find out how to access the seminars. We used Reinforcement Learning; but did it work?

Meet the Oystamaran


Currently, oyster bags have to be manually flipped every one to two weeks to reduce biofouling. When Michelle Kornberg was about to graduate from MIT, she wanted to use her knowledge of mechanical and ocean engineering to make the world a better place. Luckily, she found the perfect senior capstone class project: supporting sustainable seafood by helping aquaculture farmers grow oysters. "It's our responsibility to use our skills and opportunities to work on problems that really matter," says Kornberg, who now works for an aquaculture company called Innovasea. "Food sustainability is incredibly important from an environmental standpoint, of course, but it also matters on a social level. The most vulnerable will be hurt worst by the climate crisis, and I think food sustainability and availability really matters on that front."



This is a part-1 of the series of tutorials that I am writing on unsupervised/self-supervised learning using deep neural networks. In this tutorial, the focus would be on latent space implementation using autoencoder architecture and its visualization using t-SNE embedding. Before we delve into code, lets define some important concepts which we will encounter throughout the tutorial. The real-world data is often redundant with high dimensions. This poses challenges not only for computational efficiency but also hinders the modelling of the representation.

AI for business: what are training data use cases? - Creative AI


Another market strictly related to AI development is also finding room in this situation -- the market of data providers. Comprised for the most part of specialist agencies and start-ups, this sector deals with supplying various businesses with the services necessary for data training in order to develop customised AI systems. But what are training data use cases? Specifically, data providers manage data collection activities but also dedicate themselves to data validation and annotation, thus offering customers accurate and high-quality data sets that can be used for particular projects. Machine learning systems are based on data.

How Can We Make Artificial Intelligence Ethical?


Last week, we completed an eye-opening activity in one of my introductory graduate school courses. For some context, the class is designed to provide an introduction to different research paradigms within human-computer interaction (HCI) and related fields. We spent the first half of the quarter discussing the high-level elements of quality research and have recently been discussing methods to gauge the ethics and trustworthiness of scholarly research. For the activity, our professor had each of us analyze a research paper of choice and write a short 300-word snippet discussing the ethical issues either directly present in or implied from the research. We then compiled all of our articles together into a little "virtual magazine" of sorts, usable as a quick future reference when reading scholarly papers. The end result was fascinating, in particular because we were able to find a number of ethical concerns still present in actual, published research.

Naive Bayes Classifier Spam Filter Example : 4 Easy Steps


In probability, Bayes is a type of conditional probability. It predicts the event based on an event that has already happened. You can use Naive Bayes as a supervised machine learning method for predicting the event based on the evidence present in your dataset. In this tutorial, you will learn how to classify the email as spam or not using the Naive Bayes Classifier. Before doing coding demonstration, Let's know about the Naive Bayes in a brief.

Machine Learning & Artificial Intelligence with Python


Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of Computer Programs that can change when exposed to new data. Looking to master Machine Learning for your job or as a career enhancement?

Text Processing: A Step by Step Guide through Twitter Sentimental Analysis - YOUR DATA GUY


According to Taweh Beysolow, "Natural Language Processing (NLP) is a subfield of computer science that is focused on allowing computers to understand language in a'natural' way, as humans do." NLP has evolved so rapidly gaining traction in its applications inn artificial intelligence (AI). In this project, we will explore one of the most exciting NLP applications i.e. We will build a machine learning model that can categorize tweets as positive (pro-vaccine), negative (anti-vaccine) or neutral. Stay tuned and let's jump into the project.