Content strategy can help drive outstanding results for customer traction as well as investor interest. Whether you are starting a new venture, launching your product, or raising money, it is never too early to start creating content. We are going to focus on other content that is critical in establishing you and your company as a trusted source of value. There are three major trends that make a good content strategy even more important today.
We’re accustomed to the idea of machines acting like people. We’re even accustomed to the idea of machines thinking in ways that remind us of humans. The first generation of Robots we are familiar so far with were programmed by Humans. Now we are at a new stage of robotics when programmed robots are replaced by Machine Learned Robots. The goal of MLR is to design an efficient algorithm that statistically gives the right answer “almost always”.
Data certainly has the potential to grease the wheels of the digital economy, but with that are both opportunities and threats. It all boils down to privacy. Data has the potential to support the discovery of new medical treatments. It could transform healthcare for the better — and it is hard to find anyone who would not be in favour of that. But at what price? Regulators seem to have decided that in some cases the price is too high.
By interpreting the model, we can gain a much deeper understanding and address problems like bias, leakage and trust. Interpretability is the degree to which a human can consistently estimate what a model will predict, how well the human can understand and follow the model’s prediction and finally, how well a human can detect when a model has made a mistake. It goes without saying that AI systems must be secure and safeguarded against adversarial attacks.
Some of you are probably thinking about integrating an AI-based solution into your organization. Well, the good news is that you do not need to be an expert on AI, but you do need to understand the basics such as the importance of data(many articles are available for non-tech people on Medium). Once this is done, you can proceed to automate key tasks and use data to detect patterns and outcomes. Before jumping into the AI bandwagon, please ask yourself these three questions.
We are interacting with AI algorithms, in many cases without even knowing it.Many believe artificial intelligence has much more to offer. Ironically, one of the things that is preventing AI from realizing its full potential is the cloud, one of the main technologies that helped usher AI into the mainstream. The reason we still don’t see AI everywhere is not that the algorithms or technology are not there. The main reason is cloud dependency.
Machine learning can yield compelling insights within the scope of the information it has, but it lacks the wider context to know which results are truly useful. In addition, machines need people to tell them which datasets will be useful to analyze; if AI isn’t programmed to take a variable into account, it won’t see it. Business users must sometimes provide the context -- as well as plain common sense -- to know which data to look at, and which recommendations are useful.
What’s less well covered by the media, but still crucial to business growth, is what’s happening behind the scenes in application development and testing. The DevOps function - in theory the seamless integration of app development, testing and quality assurance - is increasingly being recognised as a strategic business function, as it powers the delivery of products and services with maximum efficiency, speed and quality. Innovations in this field may be make or break for a business.So let’s have a look at the game changing innovations in 2019.
Every Data Science project starts with a problem you aim to solve. It’s important to keep this in mind. Too often, Data Scientists run around looking to solve problems with Machine Learning. It should be the other way around. As a real-world Data Scientist, you should be aware of the following challenges. You need to convince management and stakeholders to sponsor your new project. Check for the right licensing when incorporating existing models or datasets. Most of the work you’re doing is research and data preparation.
Data scientists who are developing their first tensorflow models often struggle with the non-obvious behavior of some parts of the framework, which are hardly understandable and quite complicated to debug. The main point is that making a lot of mistakes when working on this library is perfectly fine, and for any other thing it is perfectly fine too, and asking questions, diving deep into the docs and debugging every goddamn line is very much okay too. Everything comes with practice, and hope this article will be able to make this practice a bit more pleasant and interesting.
Business leaders in advanced economies see cyberattacks as their single biggest threat, even more so than terrorist attacks. This is no surprise because the business risks associated with cybercrime are growing along with companies' ever-increasing dependence on technology. Moreover, the massive growth in the use of smart devices has opened up a universe of new ways for cybercriminals to launch attacks through large-scale botnets. Modern corporate innovation and growth must be balanced against cyber-risk and IT stability.
Full stack development is a buzz word nowadays. More and more companies are hiring full stack developers to save their time and cost. But most of the people still confused about the homonyms like Full stack developers, MEAN stack developers, MERN stack developers, etc. What is full stack developer? How to become a full stack developer? What does a full stack developer do? How to hire full stack developers? Let’s find answers in this article!!
Part 1 of this article discussed a few simple techniques that helped with initial scalability of machine learning… and hopefully with reducing manual ops. Since then, despite a few production hiccups due the lack of high availability, life has been pretty good. However, traffic soon starts to increase, data piles up, more models need to be trained, etc. Technical and business stakes are getting higher, and the current architecture will go underwater soon. This post focuses on scaling training to a large number of machines.
The pressing question enterprises rapidly need to answer if they want to remain competitive in the future, is how they can overcome security and collaboration challenges? One option to consider would be to decentralise the back-end infrastructure that underpins data processes using a private blockchain. Technologies such as distributed ledgers offer a viable solution. Although the technology is still in its relative infancy when compared to other technologies associated with digital transformation, its role in the corporate world is growing fast.
One thing you should do is build a portfolio of your personal machine learning projects. But, how to do that? I’ve seen hundreds of examples of personal projects that ranged from very good to very bad. So in this post, I’ll tell you how. If I had to summarize the secret to a great ML project in one sentence, it would be: Build a project with an interesting dataset that took obvious effort to collect and make it as visually impactful as possible.
Artificial intelligence has become a part of our day-to-day lives many times in a single day and most of us aren’t even aware of this. While there is a buzz on various emerging and hot technology platforms AI has arrived in 2019 where it will be leveraged in real-time within the enterprise and create true ROI as a result. It is true a few on the list like autonomous things, smart spaces, IoT, and even the empowered edge are used in some capacity today but true industry adoption of AI-centered tech initiatives are becoming mainstream in 2019.
It is now universally agreed that DevOps has revolutionised testing and development - and it is here to stay. So, there's a lot to consider, and it’s a complex road to getting it right. But, ultimately, we believe that leaders who don’t adequately support DevOps within their organisations — whether it’s in getting the right tools, hiring the right teams, employing the right processes or backing new ways of working — will pay dearly for their decisions in the long term.
Keras is the recommended library for beginners since its learning curve is very smooth compared to others. Keras is a Python library that provides, in a simple way, the creation of a wide range of Deep Learning models using as backend other libraries such as TensorFlow, Theano or CNTK. Although Keras is currently included in Tensorflow package, but can also be used as a Python library. To start in the subject I consider that this second option is the most appropriate.
There is a stark difference between large data and big data. Using Pandas with large data could help you explore another useful feature in Pandas to deal with large data by reducing memory usage and ultimately improving computational efficiency. Typically, Pandas has most of the features that we need for data wrangling and analysis. Pandas is seriously a game changer when it comes to cleaning, transforming, manipulating and analyzing data. In simple terms, Pandas helps to clean the mess.
It would seem that, finally, technology had appeared which would enable us to create, digitize, and transmit value, just as we create and transmit information on the classic Internet. A given blockchain-ecosystem, whatever it may be, is unlikely in itself to become what we could call the Internet of Value, just as WiFi could not become the internet. But all of them working together can. However, this will happen only in the event of the emergence of a Layer 3 technology which can ensure their interoperability.
Every firm is unique. Even within narrow verticals, the overlap in what two different companies need is smaller than you might think. Don’t try to fit your business into a box. If your business has a large amount of data and you are asking yourself, “How can I use AI to build something smart from our data?” Machine learning is just a tool to automate pattern discovery and then make smart predictions based on those patterns. Most of the time, it’s about improving an existing process by making it a little bit smarter.
Product-related risks could refer to the definition and the actual development/ implementation of Minimum Viable Product. A poorly-defined product, regardless of how well-built, will probably fail to solve the problem and deliver value to its users. Poor implementation of a well-defined product will also fail to create value to the user. Engineering-heavy startups tend to put more effort than needed on the technical aspects of the product. You need to apply agile and experimentation principles, an effective way to improve your product and create value to your users.
So you want to build a ML model. No Machine Learning is easier to manage than no Machine Learning. Figuring a way to use high-level services could save you weeks of work, maybe months. In this series of posts, we’ll discuss how to train ML models and deploy them to production, from humble beginnings to world domination. Along the way, we’ll try to take justified and reasonable steps, fighting the evil forces of over-engineering.
Docker is a platform to develop, deploy, and run applications inside containers. Docker is essentially synonymous with containerization. If you’re a current or aspiring software developer or data scientist, Docker is in your future. Don’t fret if you aren’t yet up to speed — this article will help you understand the conceptual landscape — and you’ll get to make some pizza along the way. By the end of the series (and with a little practice) you should know enough Docker to be useful.