A Year in Computer Vision
This piece simply aims to shed some light on the biggest Computer Vision advancements. And hopefully ground some of these advancements in a healthy mix of expected near-term societal-interactions and, where applicable, tongue-in-cheek prognostications of the end of life as we know it...
The impossibility of intelligence explosion
In this post, I argue that intelligence explosion is impossible — that the notion of intelligence explosion comes from a profound misunderstanding of both the nature of intelligence and the behavior of recursively self-augmenting systems. I attempt to base my points on concrete observations about intelligent systems and recursive systems...
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Data Science Articles & Videos
Analyzing 1000+ Greek Wines With Python
One of my most enjoyable guilty pleasures has always been web scraping. Especially during the past year I have scraped countless websites, both for fun and profit. From niche and mainstream e-shops to news outlets and literary blogs, it is amazing how much interesting and clean data one can get by using simple tools like BeautifulSoup- I won't even mention what a joy Chrome's Headless mode has been. In this post I'll play with the data I scraped from a Greek wine e-shop...
“The Relentless Pace of Automation”
Artificial intelligence could dramatically improve the economy and aspects of everyday life, but we need to invent ways to make sure everyone benefits...
Volcanoes of the World
Interactive visualization of the volcanoes and tectonic plates of the world. Uses d3-geo-voronoi for computing Voronoi polygons/Urquhart links, and d3-geo-zoom to handle the globe zoom/pan interaction...
Are GANs Created Equal? A Large-Scale Study
Generative adversarial networks (GAN) are a powerful subclass of generative models. Despite a very rich research activity leading to numerous interesting GAN algorithms, it is still very hard to assess which algorithm(s) perform better than others. We conduct a neutral, multi-faceted large-scale empirical study on state-of-the art models and evaluation measures...
Population based training of neural networks
In our most recent paper, we introduce a new method for training neural networks which allows an experimenter to quickly choose the best set of hyperparameters and model for the task. This technique - known as Population Based Training (PBT) - trains and optimises a series of networks at the same time, allowing the optimal set-up to be quickly found...
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model...
Intern, Data Science - Foot Locker Inc - NYC
As an Intern you will learn about the retail and ecommerce industry; participate in a collaborative business project, volunteer opportunities, corporate training. Be a part of the team that leads innovation in the athletic ecommerce industry and wear cool kicks to work every day!
Collaborate with associates on various levels and across functions to discover insights, causalities, create predictive models, machine-learning algorithms and other transformational initiatives Gain familiarity with tools and techniques, such as: exploratory data discoveries, data science research, data mining, building data products...
Training & Resources
Save The State Of A TensorFlow Model With Checkpointing
Today, we’re going to show how we can take an existing model that is working in training as we would like and how to save the states of the model so that we can use it in a separate script. This is called checkpointing....
Sequence Modeling With CTC
A visual guide to Connectionist Temporal Classiﬁcation, an algorithm used to train deep neural networks in speech recognition, handwriting recognition and other sequence problems...
Doing Strange Things with Attention
Talk by Colin Raffel at AI With The Best. Colin is a Research Scientist (formerly a resident) at Google Brain, where he is working on unsupervised learning, machine learning security, and models for sequential data...
The Lady Tasting Tea:
How Statistics Revolutionized Science in the Twentieth Century
An insightful, revealing history of how mathematics transformed our world...
"I have taken courses in statistics, taught it many times and solved several statistical problems that have appeared in journals. But until I read this book, I never really thought about it in so deep and philosophical a manner..."...
For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page...