Better encoding of categorical data can mean better model performance. In this series, I’ll introduce you to a wide range of encoding options from the Category Encoders package for use with scikit-learn in Python. Use Category Encoders to improve model performance when you have nominal or ordinal data that may provide value. In this article we’ll discuss terms, general usage and five classic encoding options: Ordinal, One Hot, Binary, BaseN, and Hashing.
Interconnected IoT devices are deployed primarily to collect data. Algorithms analyze numerous data parameters and identify trends that then enable applications to deliver innovative services. Because data is at the center of IoT, and because this data is often associated with the people who are ultimately the recipients of the services, keeping the data confidential is paramount. We discuss the three most important things you should do before you dive into IoT, and how you can identify common pitfalls to ensure you adopt this new technology with confidence.
Artificial Intelligence techniques have traditionally been divided into two categories; Symbolic A.I. and Connectionist A.I. The latter kind have gained significant popularity with recent success stories and media hype, and no one could be blamed for thinking that they are what A.I. is all about. There have even been cases of people spreading false information to diverge attention and funding from more classic A.I. research and development. The truth of the matter is that each set of techniques has its place. Each has its own strengths and weaknesses, and choosing the right tools for the job is key.
Descriptive Statistical Analysis helps you to understand your data and is a very important part of Machine Learning. This is due to Machine Learning is all about making predictions. On the other hand, statistics is all about drawing conclusions from data, which is a necessary initial step. In this post, you will learn about the most important descriptive statistical concepts. They will help you understand better what your data is trying to tell you, which will result in an overall better machine learning model and understanding.
Robotic process automation (RPA), machine learning and artificial intelligence (AI) will continue to significantly impact the legal profession, and professionals will need to adapt to and embrace these new technologies to future-proof their careers. Some of the new technology removes the need for lawyers to perform the process-driven and repetitive tasks, like drafting and checking documents, and allows them to focus on more strategic and high impact activities for their clients. Some tools in the AI space make contract review much quicker, with less human errors.
While enterprise mobile app has become a mainstay of adopting fintech, new technologies like artificial intelligence (AI) is playing an elementary role in shaping such apps. While the financial sector is fast adopting AI for its applications, it is essential to have a look at the specific ways it is going to reshape the entire fintech mobile apps. Naturally, all fintech apps today or tomorrow have to come to terms with this technology. Though until now it cannot replace human beings, it can make financial services more personalised and customer-centric than ever before.
By analyzing and comparing the examples, neural networks create complex mathematical functions with thousands of parameters, which can make statistical predictions and classify new data. Well-trained neural networks can produce very accurate results, sometimes even better than humans. But the problem is we don’t know how they work. Even the engineers who build deep learning models often can’t make sense of the logic behind thousands and millions of parameters that constitute the neural networks.
IoT security issues arise from ill-advised prioritization and the inherently short-term culture of the tech world. Security should be seen as a fundamental requirement for any IoT product—even MVPs. As the attitude of consumers and regulators shifts around those matters, it's becoming a simple matter of good business. Frankly, given the virulence and widespread nature of cyber threats, the need to take security seriously and embed it natively into IoT products should be seen as a simple matter of common sense for product developers and investors.
Adopting IoT tools for the retail environment allows customers to interact both directly and indirectly with everything in the store. This presents incredible business opportunities. It can be daunting trying to step into the IoT space, though, and that fact keeps many businesses from embracing a lucrative transformation. Delivering a personalized, unique experience with IoT devices presents more than a simple opportunity to boost sales. You also establish a closer relationship with buyers and potential buyers, generating customer affinity and increasing brand equity.
Today's world revolves around digital technologies. But what if all our apps suddenly stopped working? It is, therefore, imperative that developers deliver continuous quality throughout the entire software development lifecycle. While automation is a key factor in the DevOps lifecycle and makes continuous testing a reality, there are hurdles that deter development teams from embracing an earnest automation initiative. It's time to make the software development lifecycle continuous. Let's break down four challenges teams face with AI, open source and continuous testing in the DevOps lifecycle.
Equipment deployed in construction, agriculture, and healthcare can benefit from IoT-enabled predictive maintenance and asset tracking. There’s also a developing opportunity to use IoT to enable new business models for providers of such assets that can shift to delivering them on an “as-a-service” basis. Cloud-based IoT connectivity management platforms can support delivery of such applications on a flexible and cost-effective basis, even enabling dynamic pricing changes. Connectivity among people, things, and businesses is increasing exponentially. Old business models and processes are being rethought and new ones are emerging.
Within the field of computer networking, there are examples of solutions that have been implemented to understand infrastructure dependencies. As an example, Voice over Internet Protocol (VoIP) phones and network switches can determine the precise point of connectivity using a protocol known as Link Layer Discovery Protocol. In the Internet of Things (IoT), such a nifty standardized mechanism to determine dependencies does not exist. Here we explore examples of where sensors with this embedded ability would be useful and would contribute to the overall better experience and greater reliability.
In this article you’ll learn how to speed up your Docker build cycles and create lightweight images. One of Docker’s strengths is that it provides caching to help you more quickly iterate your image builds. When building an image, Docker steps through the instructions in your Dockerfile, executing each in order. As each instruction is examined, Docker looks for an existing intermediate image in its cache that it can reuse instead of creating a new (duplicate) intermediate image.
With more businesses adopting devops—and even more experiencing a need for faster development cycles—it’s imperative that executives and devops practitioners work together to ensure success. Despite devops gaining momentum, teams are still struggling to transform their current stack to better accommodate an accelerating pipeline. Upon closer examination, part of the problem is that these two groups are on very different pages when it comes to strategy customer experience and progress. CIOs have an optimistic view of the state of devops. Now’s the time to align CIO goals and devops practitioners’ realities.
Is your organization data-driven? Then you have probably wrestled with the issue of creating a data-driven culture. Having a data-driven culture means that data is the fundamental building block of your team. It means that every team member has a data-driven mindset. It means that every single decision maker uses data as their main evaluation asset. It means that every project uses, generates and pivots on data. It means that your team is constantly leveraging data as a strategic asset. But how do you get there?
In this post, you will learn about the mathematical objects of Linear Algebra that are used in Machine Learning. You will also learn how to multiply, divide, add and subtract these mathematical objects. You will also learn about the most important properties of Matrices and why they enable us to make more efficient computations. On top of that, you will learn what inverse- and transpose Matrices are and what you can do with it. Although there are also other parts of Linear Algebra used in Machine Learning, this post gives you a proper introduction to the most important concepts.
Machine learning methods are used in a wide range of areas. In this post, I will discuss a few of the ways that machine learning is used within the education space – specifically K-12. Machine learning has the potential of making a significant impact in this space, so let’s look at some of those areas.
IoT-enabled services are being adopted to provide user-friendly and aesthetic ways to address retail loss prevention. However, certain post-deployment observations have revealed some issues with IoT, relating to customer experience and the technology's ineffectiveness in addressing the real reasons for which it was originally deployed. IoT technology alone may not be the answer to solve certain business problems. As we have seen with the retail article tethering case, sometimes traditional and technological solutions may be complementary and have to co-exist, unless the technology is robust enough to overcome all possible user experience issues associated with its adoption.
One of the big promises of IoT is understanding the physical world around us and taking action based on insights and observations. Often, every millisecond counts—especially for use cases like earthquake monitoring. We need to build real-time networks for these mission-critical scenarios that can keep up. We have sensors everywhere distributed across nature and remote areas of the world—always on and always streaming new readings as they happen. This has transformed our understanding of how we work and live because we have more up-to-the-second data and analysis than ever before.
It is very difficult for organisations to discern which tools will bring them the most benefit, and which issues they need to plan for. New technological developments provide the platform for the next generation of innovation, as we’ve seen with the evolution of ‘Big Data’ into advanced analytics, machine learning, and artificial intelligence. How can businesses navigate this increasingly-complex data landscape to make the wisest investments? Here is our guide to the top seven data trends that should be on every organisation’s radar for the year ahead.
In terms of cyber security AI is as much a tool for the implementation of security measures, and the vendors selling them, as it is for cyber attackers. Every time a new network security technology is introduced, you can be certain terms such as deep learning, machine learning, self-learning algorithms, cognitive analytics and neural networks are part of the pitch. But the industry's focus on AI as a way to boost protection against threats ignores the larger problem, which is that of 'self-learning attackers'.
Faster R-CNN, R-FCN, and SSD are three of the best and most widely used object detection models out there right now. Other popular models tend to be fairly similar to these three, all relying on deep CNN’s (read: ResNet, Inception, etc.) to do the initial heavy lifting and largely following the same proposal/classification pipeline. At this point, putting these models to use just requires knowing Tensorflow’s API. Tensorflow has a starter tutorial on using these models. Give it a try!
“Correlation does not imply causation” we all know this mantra from statistics. And we think that we fully understand it. Human (and not human) brains, being machines to find patterns, quickly understand that my coffee mug broke because it fell to the floor. One event (the falling) occurred just before the other (mug breaking) and without the first event, we would never see the second.
Containers are popular among organisations transforming their IT operations from physical, single-tenant computing resources to a more efficient service provider infrastructure model. As application deployment using container technologies grows in production environments, security processes must scale with them. Containerisation provides a number of intrinsic security benefits, such as consistent deployment models, and production container security models should take full advantage of these benefits. To get a full picture of the risks in a container cluster, organisations must automate the process of identifying, mitigating, and alerting on any risks – regardless of source or container origin.