Recurrent Neural Networks (RNN) are a powerful and robust type of neural networks and belong to the most promising algorithms out there at the moment because they are the only ones with internal memory. RNN’s are relatively old, like many other deep learning algorithms. Because of their internal memory, RNN’s are able to remember important things about the input they received, which enables them to be very precise in predicting what is coming next.
There are many ethical controversies surrounding artificial intelligence algorithms in the past few years. In tandem with advances in artificial intelligence, there is growing interest in establishing criteria and standards to weigh the robustness and trustworthiness of the AI algorithms that are helping or replacing humans in making important and critical decisions. With the field being nascent, there’s little consensus over the definition of ethical and trustworthy AI, and the topic has become the focus of many organizations, tech companies and government institutions.
Each day brings a new set of challenges for early stage ventures. Decisions impact where you spend money, who you hire, and where to focus. The start-up roller coaster makes maintaining a disciplined decision-making process seem impossible. Everything looks important. But they are not. And everything is not urgent. Making good business decisions starts with having a clear understanding of who you are and what you want to be. Start with a target end state and then build a mission and vision statement. Here are 4 simple steps to help make fast, high quality decisions.
You want to learn data science on your own. Then, set explicit learning goals. Aim for short-term goals, which can be scoped out more realistically. Commit to your goals publicly. If you can, reach out to an industry professional, and ask them if you can keep them in the loop on your latest work with a weekly newsletter. This is an easy way to give yourself some extra motivation to make sure you have something to show for each week’s work, and doubles as a great way to grow your professional network.
Cloud computing, containerization, and container orchestration are the most important trends in DevOps. Whether you’re a data scientist, software developer, or product manager, it’s good to know Docker and Kubernetes basics. Both technologies help you collaborate with others, deploy your projects, and increase your value to employers. In this article, we’ll cover essential Kubernetes concepts. T Kubernetes is an open-source platform for managing containerized apps in production. Kubernetes is referred to as K8s for short. here are a lot of Kubernetes terms.
Whenever we talk about data science, chances are we’ll first think of those fancy stuff like AI, deep learning, machine learning, etc. But nobody talks about documentation. Documentation is often not one of the most interesting things for data scientists. But still, it’s importance is no less than other data science workflow, especially in terms of data science project management. In fact, documentation is no longer just the task done by programmers or developers. It’s something that we as a data scientist should know and be able to perform this task only a regular basis.
Finding a tax preparer in the US is not at all a cake walk. And you all share your personal information with the tax preparer including your income, marriage, kids and even your social security number. Are you feeling anxious now? In this article, I’ll be sharing 7 effective tips that will help you find the right tax preparer for your business and personal purpose as well. These tips can greatly help you to pick the right tax preparer, alerting you about common frauds.
The technology could have devastating economic consequences, or it could create the happiest workforce in history. The technology could destroy jobs, but it could put an end to tedium in the workplace. Part of the problem with studies proclaiming job losses is that they tend to focus on tasks and not the overall activities a worker might carry out. So sure, some tasks might become the preserve of automation, but that does not mean the jobs will.
What would happen if the apps that help run our lives and keep our businesses on track suddenly stopped working? You wouldn’t have easy access to things that affect your daily life, like your checking account or health-monitoring systems. From a business perspective, processes would delay, and tasks would take longer to complete. That’s why it is more important now than ever before for developers to assure continuous quality throughout the entire software development life cycle (SDLC). And it all starts with testing.
As a leader, today, both Technical Intelligence and Emotional Intelligence, often referred to as Emotional Intelligence Quotient or EQ are essential skills for long-term career success. The importance of EQ for the modern day leader cannot be underestimated. It is one of the fundamental pillars upon which your leadership is built on. If you recognize that a higher EQ would be beneficial for you, your team, and your organization, then today is the day to take those critical first steps in improving it. Enjoy the journey. The destination is far less exciting.
In the current business paradigm, replicated since by a number of online platforms, individuals willingly provide their personal information in exchange for a service. Personal data is subsequently repackaged and sold to advertisers and marketers. The unavoidable rise of the Internet of Things will only make the issue more complex, as increasingly more intrusive and personal data will start to be collected about each of us. This poses new challenges around the issue of consent and privacy:
The Chimp Paradox uses a simple analogy to explain functional brain types, dubbing one the ‘Human’ and the other ‘Chimp’. We all have them both, the Human side is logical, calm and emotionally assured brain, whereas the Chimp brain can be fiery, spontaneous and alert to help you avoid trouble. Same is the case with data chimp in Energy Management. In a vast and growing array of data feeds from energy meters, data can be hugely distracting and create emotional unease should it highlight unexpected results.
Transfer learning is all the rage in the machine learning community these days. It serves as the basis for many of the managed AutoML services and now figures prominently in the latest NLP research. We’re also starting to see examples of neural networks that can handle multiple tasks using transfer learning across domains. The main question at hand is: could transfer learning have applications within reinforcement learning? Compared to other machine learning methods, deep reinforcement learning has a reputation for being data hungry, subject to instability in its learning process.
Machine learning/Deep Learning/AI are fancy number crunchers and they can have some amazing results given good data, however, the first step is to properly understand your data so you can make informed decisions about what algorithms and data cleaning methods to use. One of the first things in understanding your data is to know what kind of data you have! Here are the 4 most common types of data that you will come across.
Fintech has become a key component of modern financial companies and their offerings. Whether it is insurance, home loans, savings, and term deposit products or business loans, every financial product today depends on Fintech. As Fintech can only grow thanks to smarter innovations that keep pouring, for startups, it offers a never-before opportunity. But, how can you build an innovative and business-driven fintech mobile app to propel growth for your startup? What are the key considerations when developing a new fintech app? Let's explain a few of them here.
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.