Over the past few months, I have been collecting AI cheat sheets. From time to time I share them with friends and colleagues and recently I have been getting asked a lot, so I decided to organize and share the entire collection. To make things more interesting and give context, I added descriptions and/or excerpts for each major topic. This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it. Scikit-learn (formerly scikits.learn) is a free softwaremachine learninglibrary for the Python programming language.

A representation of a neural network provides a backdrop to a fish larva's beating heart. The advent of deep learning, a powerful form of machine learning, has led to rapid advancements in areas such as speech recognition, visual object recognition, genomics and drug discovery. These methods are characterized by multiple processing layers that can tease out intricate patterns and structures in very large, complex data sets. Now, a team of European researchers has incorporated deep learning algorithms into a light-field microscope to enhance both its reconstruction speed and image quality (Nat. Methods, doi: 10.1038/s41592-021-01136-0). The results significantly extend the capabilities of light-field microscopy for whole-brain or whole-animal imaging of living specimens for biomedical research.

Machine learning is hot right now, and for good reason. We're going to break down what you need to know about what goes into a model and give you four machine learning models your business should have in production right now. The Lead/Opportunity Conversions Model The lifeblood of every business is new leads and opportunities. Having a machine learning model in place to predict where you're more likely to convert those leads can be an effective guide to growth. The Attrition/Customer Retention Model Once you have a customer in your ecosystem, it's in your best interest to keep that customer for the long haul. The attrition/customer retention model can tell you who has a high propensity to churn, so you can market to your existing base effectively. The Lifetime Value Model Increasing the lifetime value of your customers or clients is critical. Having a model in place that offers behavior-driven insight will help you keep your customers in your pipeline longer.

Right now, a minivan with no one behind the steering wheel is driving through a suburb of Phoenix, Arizona. And while that may seem alarming, the company that built the "brain" powering the car's autonomy wants to assure you that it's totally safe. Waymo, the self-driving unit of Alphabet, is the only company in the world to have fully driverless vehicles on public roads today. That was made possible by a sophisticated set of neural networks powered by machine learning about which very is little is known -- until now. For the first time, Waymo is lifting the curtain on what is arguably the most important (and most difficult-to-understand) piece of its technology stack. The company, which is ahead in the self-driving car race by most metrics, confidently asserts that its cars have the most advanced brains on the road today. Anyone can buy a bunch of cameras and LIDAR sensors, slap them on a car, and call it autonomous. But training a self-driving car to behave like a human driver, or, more importantly, to drive better than a human, is on the bleeding edge of artificial intelligence research. Waymo's engineers are modeling not only how cars recognize objects in the road, for example, but how human behavior affects how cars should behave. And they're using deep learning to interpret, predict, and respond to data accrued from its 6 million miles driven on public roads and 5 billion driven in simulation.

But when risk models are built using AI, it may be much harder to pin down what insurance companies are basing higher premiums on, he said. For instance, if companies use neural nets, an AI technique that's the basis for deep learning, the resulting model is basically an opaque box. Insurance companies would know what factors were used to train their AI model, and using the models to evaluate new customers would be as simple as feeding it the same types of inputs, but companies wouldn't know how the model internally related those factors to risk and which inputs are more important.

Explainable AI (XAI) is key to establishing trust among users and fighting the black-box nature of machine learning models. In general, XAI enhances accountability and reliability in machine learning models. For a long time, tech giants like Google, IBM and others have poured resources on explainable AI to explain the decision-making process of such models. Below are the top free resources to understand Explainable AI (XAI) in detail. About: Explainable Machine Learning with LIME and H2O in R is a hands-on, guided introduction to explainable machine learning.

With Canadian infection rates recently breaking the 1 million mark and deaths topping 23,000 and counting, and the global infection rate reaching 132 million, with nearly 3 million deaths to date, intervention is critical. "By targeting human proteins necessary for the coronavirus lifecycle, we are developing therapeutic strategies applicable to future strains or variants" said Dr. Bo Wang of the Vector Institute. "Unlike high throughput screens, accurate AI predictions aimed at specific human-based, antiviral targets provided us with a risk-adjusted strategy to search for drug-repurposing opportunities using scientifically accurate experiments with high biological relevance." The discovery of capmatinib's antiviral activity is a shining example of the power of collaborative research programs. The Vector Institute team's graph convolutional network (GCN) identified possible human targets relevant to COVID-19, which fed directly into Cyclica's PolypharmDB - a database for finding repurposing opportunities, generated using Cyclica's deep learning approach MatchMaker.

Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) projects without sharing sensitive data, such as, patient records, financial data, or classified secrets. Open Federated Learning (OpenFL https://github.com/intel/openfl) is an open-source framework for training ML algorithms using the data-private collaborative learning paradigm of FL. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and deep learning frameworks. Here, we summarize the motivation and development characteristics of OpenFL, with the intention of facilitating its application to existing ML model training in a production environment. Finally, we describe the first use of the OpenFL framework to train consensus ML models in a consortium of international healthcare organizations, as well as how it facilitates the first computational competition on FL.

This chapter provides a summary of recent developments harnessing the data revolution to realize the science goals of Gravitational Wave Astrophysics. This is an exciting journey that is powered by the renaissance of artificial intelligence, and a new generation of researchers that are willing to embrace disruptive advances in innovative computing and signal processing tools. In this chapter, machine learning refers to a class of algorithms that can learn from data to solve new problems without being explicitly re-programmed. While traditional machine learning algorithms, e.g., random forests, nearest neighbors, etc., have been used successfully in many applications, they are limited in their ability to process raw data, usually requiring time-consuming feature engineering to preprocess data into a suitable representation for each application. On the other hand, deep learning algorithms can learn patterns from unstructured data, finding useful representations and automatically extracting relevant features for each application. The ability of deep learning to deal with poorly defined abstractions and problems has led to major advances in image recognition, speech, computer vision applications, robotics, among others [1]. The following sections describe a few noteworthy applications of modern machine learning for gravitational wave modeling, detection and inference. It is the expectation that by the time this chapter is published, the ongoing developments at the interface of artificial intelligence and extreme-scale computing will have leapt forward, making this chapter a reminiscence of a fast-paced, evolving field of research. The chapter concludes with a summary of recent applications at the interface of deep learning and high performance computing to address computational grand challenges in Gravitational Wave Astrophysics.

We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for sample quality using gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128$\times$128, 4.59 on ImageNet 256$\times$256, and 7.72 on ImageNet 512$\times$512, and we match BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3.85 on ImageNet 512$\times$512. We release our code at https://github.com/openai/guided-diffusion