If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The features are sorted by mean( Tree SHAP) and so we again see the relationship feature as the strongest predictor of making over $50K annually. By plotting the impact of a feature on every sample we can also see important outlier effects. For example, while capital gain is not the most important feature globally, it is by far the most important feature for a subset of customers. The coloring by feature value shows us patterns such as how being younger lowers your chance of making over $50K, while higher education increases your chance of making over $50K. We could stop here and show this plot to our boss, but let's instead dig a bit deeper into some of these features.
In this post, I shall explain object detection and various algorithms like Faster R-CNN, YOLO, SSD. We shall start from beginners' level and go till the state-of-the-art in object detection, understanding the intuition, approach and salient features of each method. Image classification takes an image and predicts the object in an image. What would our model predict? To solve this problem we can train a multi-label classifier which will predict both the classes(dog as well as cat).
You should always set the seed before calling train. Probably not the most amazing \(R 2\) value you have ever seen, but that's alright. Note that calling the model fit displays the most crucial information in a succinct way. Let's move on to a classification algorithm. It's good practice to start with a logistic regression and take it from there.
A few weeks ago, a dejected CTO told me it took his team three weeks to build a machine learning model. I told him a model in just three weeks sounded great, and he agreed. Because eleven months later, the model was still sitting on a shelf. That gap between great AI prototypes and AI in operation is starting to be a common theme as AI and machine learning make contact with the real world. The reason is … Actually, there are a lot of reasons and we can look at a bunch of them, but the reason underneath all the other reasons is that data doesn't sit still, and never will.
In this project I have attempted to create supervised learning models to assist in classifying certain employee data. I pre-processed the data by removing one outlier and producing new features in Excel as the data set was small at 1056 rows. Some categorical features were also converted to numeric values in Excel. For example, Gender was originally "M" or "F", which was converted to 0 and 1 respectively. I also removed employee number as it provides no value as a feature and could compromise privacy.
Algorithmic defenses against adversarial examples remain an extremely open and challenging problem, with recent state-of-the-art defenses on ImageNet still achieving only 27.9% and 46.7% top-1 accuracy for white- and black-box PGD attacks, respectively, as of March 2018 \citepkannan2018adversarial. Unfortunately, despite the explosive emergence of defense strategies, there does not appear to be an easy algorithmic fix for the adversarial problem available in the short term. For example, one recent analysis investigated a series of promising methods that relied on gradient obfuscation, and demonstrated that they could be quickly broken \citepathalye2018obfuscated. Despite this, we also note that principled approaches to adversarial robustness are beginning to show promise. For example, several papers have demonstrated what appears to be both high accuracy and strong adversarial robustness on smaller datasets such as MNIST, \citepmadry2017towards,kannan2018adversarial, and there have also been several results including theoretical guarantees of adversarial robustness, albeit on small datasets and/or with still-insufficient accuracy \citepkolter2017provable, raghunathan2018certified, dvijotham2018dual.
Summary: GDPR carries many new data and privacy requirements including a "right to explanation". On the surface this appears to be similar to US rules for regulated industries. We examine why this is actually a penalty and not a benefit for the individual and offer some insight into the actual wording of the GDPR regulation which also offers some relief. GDPR is now just about 60 days away and there's plenty to pay attention to especially in getting and maintaining permission to use a subscriber's data. If you're just starting out in the EU there are some new third party offerings that promise to keep track of things for you (Integris, Kogni, and Waterline all emphasized this feature at the Strata Data San Jose conference this month).
The debate on how artificial intelligence (AI) could shape the way we work tends to take place on a grand scale. We talk about a future where driverless vehicles will deliver our goods from factories filled with armies of robot workers. Adam Reynolds, CEO of webexpenses, discusses how we may be missing the more mundane and practical ways that AI is already reshaping our everyday working lives, and transforming the way businesses operate. Thanks to a new generation of AI-based systems and tools we can eliminate a whole swathe of tedious and repetitive work and home tasks – bringing intuition, help and time-saving benefits to our lives. These can be found within every industry – from systems designed to root out important clauses from large volumes of legal documents to medical software that identifies potential risks in patient data.
Stochastic Signal Analysis is a field of science concerned with the processing, modification and analysis of (stochastic) signals. Anyone with a background in Physics or Engineering knows to some degree about signal analysis techniques, what these technique are and how they can be used to analyze, model and classify signals. Data Scientists coming from a different fields, like Computer Science or Statistics, might not be aware of the analytical power these techniques bring with them. In this blog post, we will have a look at how we can use Stochastic Signal Analysis techniques, in combination with traditional Machine Learning Classifiers for accurate classification and modelling of time-series and signals. At the end of the blog-post you should be able understand the various signal-processing techniques which can be used to retrieve features from signals and be able to classify ECG signals (and even identify a person by their ECG signal), predict seizures from EEG signals, classify and identify targets in radar signals, identify patients with neuropathy or myopathyetc from EMG signals by using the FFT, etc etc. In this blog-post we'll discuss the following topics: You might often have come across the words time-series and signals describing datasets and it might not be clear what the exact difference between them is.