This article is part of "AI education", a series of posts that review and explore educational content on data science and machine learning. How much math knowledge do you need for machine learning and deep learning? Some people say not much. Both are correct, depending on what you want to achieve. There are plenty of programming libraries, code snippets, and pretrained models that can get help you integrate machine learning into your applications without having a deep knowledge of the underlying math functions.
No-code environments in machine learning have become increasingly popular due to the fact that almost anybody who needs machine learning, whatever field they may be in, can use these tools to build models for themselves. WEKA is one of the early no-code tools that was developed but is very efficient and powerful. WEKA can be used to implement state of the art machine learning and deep learning models and can support numerous file formats. In this article, we will learn about how to use WEKA to pre-process and build a machine learning model with code. WEKA can be used in Linux, Windows or Mac operating systems and you can download this from the official website here.
Companies in all business sectors are competing to recruit top-notch AI teams, but are these investments productive? With millions worldwide working in AI now, and over 90% of mid-size and larger companies having specialized AI or Data Science teams, researchers and engineers in this field are literally drowning in the pace of innovation. Per day, an AI expert needs to scan several hundred new research publications to stay up to date. Leading researchers, like Yoshua Bengio and Yann LeCun, openly admit they find it impossible to keep up. Amsterdam based startup Zeta Alpha is now launching AI Research Navigator, a new deep learning-based search platform, to help AI experts with this.
To learn the best, you must learn from the finest. Geoffrey Hilton is called the Godfather of Deep Learning in the field of data science. Mr. Hinton is best known for his work on neural networks and artificial intelligence. A Ph.D. in artificial intelligence, he is accredited for his exemplary work on neural nets. The co-founder of the term, "Data Science", Jeff Hammerbacher developed methods and techniques for capturing, storing and analysing a large amount of data.
Join the most comprehensive Flutter & Deep Learning course on Udemy and learn how to build amazing state-of-the-art Deep Learning applications! Do you want to learn about State-of-the-art Deep Learning algorithms and how to apply them to IOS/Android apps? Then this course is exactly for you! You will learn how to apply various State-of-the-art Deep Learning algorithms such as GAN's, CNN's, & Natural Language Processing. In this course, we will build 6 Deep Learning apps that will demonstrate the tools and skills used in order to build scalable, State-of-the-Art Deep Learning Flutter applications!
TLDR; The Azure ML Python SDK enables Data scientists, AI engineers,and MLOps developers to be productive in the cloud. This post highlights 10 examples every cloud AI developer should know, to be successful with Azure ML. If you are new to Azure you can get a free subscription using the link below. The scripts in this example are used to classify iris flower images to build a machine learning model based on scikit-learn's iris dataset the code can easily be adapted to any scikit-learn estimator. This example shows you how to run your TensorFlow training scripts at scale using Azure Machine Learning's TensorFlow estimator class.
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It's achieving results that were not possible before.
Dublin start-up Ubotica has brought its AI technology into orbit aboard a next-gen ESA satellite. Dublin-based Ubotica Technologies has announced that its AI tech has gone into orbit aboard the Earth observation satellite PhiSat-1, which was launched along with 52 other satellites on a European Space Agency (ESA) Vega rocket yesterday (3 September). The satellite is part of a programme funded by ESA and supported by Enterprise Ireland, in which deep-learning technology for the in-orbit processing of Earth observation data is being deployed on a European satellite for the first time. Ubotica's CVAI technology, built on the Intel Movidius Myriad 2 vision processing unit, will allow the satellite to make its own decisions rather than relying on humans down on the planet's surface, resulting in faster, more efficient applications being deployed on the satellite. In this instance, Ubotica's AI tech is being tasked with automatic cloud detection on images captured by the satellite's advanced hyperspectral sensor.
The Cambridge Dictionary defines "bootstrap" as: "to improve your situation or become more successful, without help from others or without advantages that others have." While a machine learning algorithm's strength depends heavily on the quality of data it is fed, an algorithm that can do the work required to improve itself should become even stronger. A team of researchers from DeepMind and Imperial College recently set out to prove that in the arena of computer vision. In the updated paper Bootstrap Your Own Latent – A New Approach to Self-Supervised Learning, the researchers release the source code and checkpoint for their new "BYOL" approach to self-supervised image representation learning along with new theoretical and experimental insights. In computer vision, learning good image representations is critical as it allows for efficient training on downstream tasks. Image representation learning basically leverages neural networks that have been trained to produce good representations.
One of the responsible things to do when a year is ending is to reflect on it. What accomplishments you have made, what challenges did you face, what did you learn, and how you can make the remainder of the year count. One experience that I can definitely share, and hopefully it would be beneficial to readers, is being awarded the 2019 Bertelsmann Tech Scholarship and receive the Deep Learning Nanodegree from Udacity, completely free of charge. And this year, Bertelsmann Tech is opening another scholarship application, which you should definitely try if you have a passion for data and cloud tech. Many people have asked online what it was like to apply for the Bertelsmann Tech scholarship, win it, and complete the Nanodegree from Udacity.