Say you have a thousand columns and a million rows in your data set. Whichever way you look at it – small, medium or big data – you won't be able to actually look at it. Blame human nature but most of us understand a subject better when they get to see a bigger picture. Is there a way to put your data in one image and navigate it almost like you would do with a map? Deep Learning combined with Topological Data Analysis can do exactly that and more.

TL;DR A resilient Data Science Platform is a necessity to every centralized data science team within a large corporation. It serves as the foundation layer on top of which three internal stakeholders collaborate: product data scientists, central data scientists, and IT infrastructure. Figure 1: A data science platform serves three stakeholders: product, central, and infrastructure. Serverless scaling, as implemented by Algorithmia Enterprise, is horizontal scaling on-demand by encapsulating your model in a dedicated container, deploying that container just-in-time across your compute cluster, and destroying it right after execution to release resources.

But there are all sorts of smaller machine learning projects out there that people are building and using: pipelines, wrappers, high-level APIs, cleaners, etc. Many of the implemented functions share similarities with scikit-learn's API, but future addition functionality will not necessarily be restricted by this. Olson bills Data Cleaner as a "Python tool that automatically cleans data sets and readies them for analysis." The folder GCP-HPO contains all the code implementing the Gaussian Copula Process (GCP) and a hyperparameter optimization (HPO) technique based on it.

The Differentiable Neural Computer is an awesome model that DeepMind recently released. It's a memory augmented network that can perform meta-learning (learning to learn). We'll go over it's architecture details and implement it ourselves in Tensorflow. That's what keeps me going.

In this last weekly video of the course, i'll explain how memory augmented neural networks can help achieve one-shot classification for a small labeled image dataset. We'll also go over the architecture of it's inspiration (the neural turing machine). That's what keeps me going.

In 2016, we find ourselves awash in news about machine learning. Driving this wave of interest, a number of breakthroughs - many due to deep learning - have pushed the state-of-the-art in computer vision, speech recognition and natural language processing. Per Google Trends, searches for machine learning are up four-fold while searches for deep learning are up ten-fold over the last five years. These advances have cracked open viable paths towards primitive but economically impactful systems. Responding to this demand, our news outlets, blogs, technical magazines and newspapers alike, have struggled to keep up.

Summary: As deep learning expands those capabilities are finding their way into the not-for-profit community in the service of conserving the earth's wildlife and forests. The for-profit world may be driving AI but it's a solution to many problems in the not-for-profit world as well. We were particularly impressed by the use of deep learning technologies to solve problems in the pursuit of preserving natural resources including many species of animals and fish, and also including forests. For the most part the data problems that nature conservancy organizations face fall into these categories. Going back 20 years this meant putting intrepid feet on the ground with binoculars and note pads.

The days of claiming artificial intelligence as a feature that set one startup or company apart from the others are over. These days, one would be hard-pressed to find any technology company attracting venture funding or partnerships that doesn't posit to use some form of machine learning. But for companies trying to innovate in healthcare using artificial intelligence, the stakes are considerably higher, meaning the hype surrounding the buzzword can be deflated far more quickly than in some other industry, where a mistaken algorithm doesn't mean the difference between life and death. Over the past five years, the number of digital health companies employing some form of artificial intelligence has dramatically increased. CB Insights tracked 100 AI-focused healthcare companies just this year, and noted 50 had raised their first equity rounds since January 2015.

This article is part of a special insideHPC report that explores trends in machine learning and deep learning. The complete report, available here, covers how businesses are using machine learning and deep learning, differentiating between AI, machine learning and deep learning, what it takes to get started and more. The impact of AI on business functions will play out differently in different industry sectors. In financial services firms, AI's impact is expected to be felt most strongly in the area of customer interaction, and in manufacturing organizations-- product development. Health and life sciences, meanwhile, anticipate the AI impact will be greatest in management decision-making.

The Artificial Intelligence Market Forecasts 2016 -2025 across 27 Industry Sectors has provided an overview of numerous Artificial Intelligence use cases, which includes Machine Learning, machine reasoning, Deep Learning, NLP, computer vision, and many other allied technologies. According to this study, food services, consumer products, advertising, and defense (along with others mentioned above) will significantly benefit from the growth of AI in the coming years.