If you've been at machine learning long enough, you know that there is a "no free lunch" principle -- there's no one-size-fits-all algorithm that will help you solve every problem and tackle every dataset. I work for Springboard -- we've put a lot of research into machine learning training and resources. At Springboard, we offer the first online course with a machine learning job guarantee. What helps a lot when confronted with a new problem is to have a primer for what algorithm might be the best fit for certain situations. Here, we talk about different problems and data types and discuss what might be the most effective algorithm to try for each one, along with a resource that can help you implement that particular model.

One in three people are expected to develop cancer during their lifetime. With the help of AI-based algorithms, pathologists will be able to deliver better and faster diagnoses. New cancer cases will increase by almost 70% over the next two decades, from 14 million in 2012 to 22 million. In the US, the National Cancer Institute reports that one in three people are expected to develop cancer in their lifetime. Pathologists are dealing with growing caseloads while patients are increasingly expecting high-quality diagnoses and treatments, causing delays in delivering diagnoses.

Here at Zyl, we have been committed in doing Deep learning operations on mobile for more than two years now. Even before CoreML, we were here. Here's a series of our top 3 "fun" facts when we started embedding models on device (not so fun at the time): Now you must be wondering: why haven't they tested the app or read articles to avoid these issues? Well, you can find many content online, and anyone with a little skill in python might think it's easy to build a « deep learning model » for his/her next business -- especially two years ago. But between tutorial and production, there is a gap.

Almost everything you hear about artificial intelligence today is thanks to deep learning. This category of algorithms works by using statistics to find patterns in data, and it has proved immensely powerful in mimicking human skills such as our ability to see and hear. To a very narrow extent, it can even emulate our ability to reason. These capabilities power Google's search, Facebook's news feed, and Netflix's recommendation engine--and are transforming industries like health care and education. But though deep learning has singlehandedly thrust AI into the public eye, it represents just a small blip in the history of humanity's quest to replicate our own intelligence.

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Deep leaning is one of the most profound but widely recognized phenomenon taking place in the world of Information Technology. That statement opened a talk by Chris Rowen, CEO and co-founder of BabbleLabs, to a group of semiconductor industry executives during a recent Mentor, a Siemens Business, Summit hosted by its Emulation Division. Rowen explained that some basic notions about what makes deep learning different are needed to understand the implications at the application level, especially around vision and speech recognition, BabbleLabs' specialization. He also tailored his talk to outline what deep learning means for the semiconductor industry. As he noted, everyone has been exposed to the AI hype.

UK startup Fabula AI reckons it's devised a way for artificial intelligence to help user generated content platforms get on top of the disinformation crisis that keeps rocking the world of social media with antisocial scandals. Even Facebook's Mark Zuckerberg has sounded a cautious note about AI technology's capability to meet the complex, contextual, messy and inherently human challenge of correctly understanding every missive a social media user might send, well-intentioned or its nasty flip-side. "It will take many years to fully develop these systems," the Facebook founder wrote two years ago, in an open letter discussing the scale of the challenge of moderating content on platforms thick with billions of users. "This is technically difficult as it requires building AI that can read and understand news." But what if AI doesn't need to read and understand news in order to detect whether it's true or false? Step forward Fabula, which has patented what it dubs a "new class" of machine learning algorithms to detect "fake news" -- in the emergent field of "Geometric Deep Learning"; where the datasets to be studied are so large and complex that traditional machine learning techniques struggle to find purchase on this'non-Euclidean' space. The startup says its deep learning algorithms are, by contrast, capable of learning patterns on complex, distributed data sets like social networks.

Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these interactive factors, and to reveal such relationship requires both comprehensive datasets and powerful algorithms. In the 2018 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the genotype and yield performances of 2,267 maize hybrids planted in 2,247 locations between 2008 and 2016 and asked participants to predict the yield performance in 2017. As one of the winning teams, we designed a deep neural network (DNN) approach that took advantage of state-of-the-art modeling and solution techniques. Our model was found to have a superior prediction accuracy, with a root-mean-square-error (RMSE) being 12% of the average yield and 50% of the standard deviation for the validation dataset using predicted weather data. With perfect weather data, the RMSE would be reduced to 11% of the average yield and 46% of the standard deviation. Our computational results suggested that this model significantly outperformed other popular methods such as Lasso, shallow neural networks (SNN), and regression tree (RT).

We introduce a neural architecture for navigation in novel environments. Our proposed architecture learns to map from first-person views and plans a sequence of actions towards goals in the environment. The Cognitive Mapper and Planner (CMP) is based on two key ideas: a) a unified joint architecture for mapping and planning, such that the mapping is driven by the needs of the task, and b) a spatial memory with the ability to plan given an incomplete set of observations about the world. CMP constructs a top-down belief map of the world and applies a differentiable neural net planner to produce the next action at each time step. The accumulated belief of the world enables the agent to track visited regions of the environment. We train and test CMP on navigation problems in simulation environments derived from scans of real world buildings. Our experiments demonstrate that CMP outperforms alternate learning-based architectures, as well as, classical mapping and path planning approaches in many cases. Furthermore, it naturally extends to semantically specified goals, such as 'going to a chair'. We also deploy CMP on physical robots in indoor environments, where it achieves reasonable performance, even though it is trained entirely in simulation.

Motivated by the recent potential of mass customization brought by whole-garment knitting machines, we introduce the new problem of automatic machine instruction generation using a single image of the desired physical product, which we apply to machine knitting. We propose to tackle this problem by directly learning to synthesize regular machine instructions from real images. We create a cured dataset of real samples with their instruction counterpart and propose to use synthetic images to augment it in a novel way. We theoretically motivate our data mixing framework and show empirical results suggesting that making real images look more synthetic is beneficial in our problem setup.