Technology Guru

In today’s article, we are going to generate realistic looking faces with Machine Learning. In order to do so, we are going to leverage Generative Adversarial Networks ( GANs ), and more specifically Deep Convolutional Generative Adversarial Networks ( DCGANs ). By the end of this post, you will be able to successfully train a GAN to sample an infinite amount of images based on a given dataset, which in our case will be human faces. Let’s start with a simple question. Can you tell which of the following images are real and which ones are fake? Do they exist? We will get back to this later, stay tuned! Meanwhile, let’s proceed with GANs and try to artificially create realistic looking faces. Deep Convolutional Generative Adversarial Networks (DCGANs) If you are completely new to the GANs field, I recommend you to check my previous article that covers its absolute basics. Even if you are not a beginner, I still recommend you to take a loo...

Text data is everywhere Whether you are an established company or working to launch a new service, you can always leverage text data to validate, improve, and expand the functionalities of your product. The science of extracting meaning and learning from text data is an active topic of research called Natural Language Processing (NLP). NLP produces new and exciting results on a daily basis and is a very large field. However, having worked with hundreds of companies, the Insight team has seen a few key practical applications come up much more frequently than any other: Identifying different cohorts of users/customers (e.g. predicting churn, lifetime value, product preferences) Accurately detecting and extracting different categories of feedback (positive and negative reviews/opinions, mentions of particular attributes such as clothing size/fit…) Classifying text according to intent (e.g. request for basic help, urgent problem) While many NLP papers and tutorials exist onlin...

Facebook's products and services are powered by machine learning. Powerful GPUs have been one of the key enablers, but it takes a lot more hardware and software to serve billions of users. Most of Facebook's two billion users have little idea how much the service leans on artificial intelligence to operate at such a vast scale. Facebook products such as the News Feed, Search and Ads use machine learning, and behind the scenes, it powers services such as facial recognition and tagging, language translation, speech recognition, content understanding and anomaly detection to spot fake accounts and objectionable content. The numbers are staggering. In all, Facebook's machine learning systems handle more than 200 trillion predictions and five billion translations per day. Facebook's algorithms automatically remove millions of fake accounts every day. In a keynote at this year's  International Symposium on Computer Architecture (ISCA) , Dr. Kim Hazelwood, the...

A recent news item went as follows: “Apple buys machine learning firm Perceptio Inc., a startup, in an attempt to bring advanced image-classifying artificial intelligence to smartphones by reducing data overhead which is typically required of conventional methods.” Another recent development was that MIT researchers were working on object recognition through flexible machine learning. Machine learning is starting to reshape how we live, and it’s time we understood what it is and why it matters. Pro Tip:  To fast-track your learning, consider Simplilearn’s comprehensive  Machine Learning Advanced Certification Training . With modules in supervised and unsupervised learning, deep learning, Spark, and live industry projects, you can become a job-ready machine learning specialist in just a few weeks ! What is Machine Learning? Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being exp...