Gradient descent is by far the most popular optimization strategy, used in machine learning and deep learning at the moment. It is used while training your model, can be combined with every algorithm and is easy to understand and implement. Therefore, everyone who works with Machine Learning should understand it’s concept. After reading this posts you will understand how Gradient Descent works, what types of it are used today and what are their advantages and tradeoffs.
I have loved being a data scientist. The job is challenging. The job market is great. I can't imagine any other job that would provide more career fulfilment for me now. This is my ideal career. Across the way, I see people that I care about trying to break into this field. Wondering what to study, what skills they need, feeling clueless about how to get that first real data science job in this market. This article is for them.
A report analysis that caught my eye was a comparison of Data Scientist salaries on the intersection of experience and education, with experience levels of 1-3 years, 4-8 years, and 9+ years, and educational attainment of Masters vs PhD. Not surprisingly, there’s an over 25% difference in median salary by category of advancing experience, along with a consistent differential between Masters and PhD of roughly 10%. Makes sense to me: obviously, experience is critical and PhD’s may well bring more of the Science/Research Methodology component of DS to the table from the get-go than do most Masters-trained practitioners.
Behind every technological innovation that hits the market, there is a team of highly skilled professionals working jointly to make the innovation a success. And it is obvious that when technologies come in, employees are expected to update their knowledge and skills to be able to leverage the technology. From bridging the talent gap to incubating the right culture to modifying the infrastructure, every small thing should be taken into account when companies plan to harness the power of any new technology.
Knowledge isn’t power. Almost everything we know is either currently on the internet, or will be soon. If we reach a stage when every person and machine has access to the same information, what will set you apart from the pack? Your power is through connection. One way you’ll stand out is by cultivating an ability to communicate knowledge in a more compelling way than other people or machines can do it. We yearn for human connection, yet few people develop their skills in this area.
What is the purpose of your resume? In my opinion, the main purpose of your resume is to land you the first interview. Have you ever applied to many tech jobs that suit your experience and skill but never heard back? Does it feel like you are sending your resume to a black hole? Then this is something you absolutely must read. After you know what the tech recruiters are looking for you can tailor your resume that lands you an interview.
Computer vision is the field of computer science that focuses on replicating parts of the complexity of the human vision system and enabling computers to identify and process objects in images and videos in the same way that humans do. Until recently, computer vision only worked in limited capacity. Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take great leaps in recent years and has been able to surpass humans in some tasks related to detecting and labeling objects.
How supercomputing could contribute to improving Machine Learning methods?The tasks of training Deep Learning networks requires a large amount of computation and, often, they also need the same type of matrix operations as the numerical calculation intensive applications, which makes them similar to traditional supercomputing applications. Deep Learning applications work very well in computer systems that use accelerators such as GPU or field-programmable gate arrays (FPGA), which have been used in the Supercomputing field for more than a decade within the walls of the supercomputing research centers.
By unifying analytics, building forecasts and accelerating analytic processes, simulation helps companies build a holistic picture of their business to optimize strategy and maximize revenue. Here are the four types of information that companies need to fuel simulation forecasting and monetize their data investments. Once a company identifies sources for these four types of data, it’s time to find an effective way to monetize it. With simulation forecasting, reliable answers are accessible – and you may need less data than you think to get meaningful, trustworthy insight.
Many organizations have grown comfortable with their business intelligence solution, and find it difficult to justify the need for advanced analytics. The advantages of advanced analytics are numerous and those advantages are based on the ability to further improve the business, increase user adoption and therefore user empowerment and accountability and, best of all, improve the bottom line and the accuracy of predictions and forecasts that will dictate the success of the business in the future.
Sometimes an interview goes wrong, and it’s not always the fault of the candidate. This person may be considered the creative/visual type, with strong expertise in building solid UI elements that can be used in web applications, but then gets asked in the interview how to solve a complex algorithm around binary trees or big O on a whiteboard. But most of the time a job doesn’t require such complexity on a daily basis. In all seriousness, having a poor interviewing process reflects very badly on the company.
We all know a friend who works in tech. And they are usually doing quite well for themselves, probably started coding since the beginning of time and often seem to be spoilt with career choices. Or are they? Here are some common myths about careers in tech and how they hold up against data. The data used here comes from a developer survey by StackOverflow, a website that a of developers frequent regularly. It’s a pretty comprehensive survey, with more than a hundred thousand responses from all over the world. Let’s get started!
Digital transformation and data protection; to some that may feel like a contradiction in terms, but in fact, the two can be interdependent. With digital transformation, there is a temptation. It’s the allure of just focusing on the deluge of information available and the potential for business advancement, if only one can successfully aggregate, interrogate and monetise it. Digital transformation and data protection, on the other hand, seem to be at odds. After-all, the principles of adding more control to data usage can feel like roadblocks on the path to becoming data driven.
AI and Machine learning have brought in limitless possibilities to businesses across industries. Now, these technologies have capabilities to help businesses implement preventive maintenance to timely take care of anything you name it. Apart from this, AI can facilitate efficiency to work and lessen the chances of errors while holding highest quality standards. With the right AI experts and right AI app development, nearly every industry can reap the benefits of the technology. Here are 10 industries which will soon be revolutionized by AI.
There are numerous benefits of using a blockchain in the healthcare industry that can help save lives, reduce costs and make healthcare centres more efficient. This is an extremely exciting time for blockchain technology particularly in the healthcare industry. The success of this partnership is yet to be determined but one thing is for certain — Blockchain is here to stay.It is also worth noting that the blockchain does not have to be limited to just one hospital.
Let’s look at several techniques in machine learning and the math topics that are used in the process.
In linear regression, we try to find the best fit line or hyperplane for a given set of data points. We model the output of our linear function by a linear combination of the input variables using a set of parameters as weights.
Do you need a graduate degree for data science? Maybe so. Maybe not. In a rapidly developing field like data science, convention can often lag considerably behind what’s optimal. As a society, our perception of the value of graduate education is one of the aspects of conventional wisdom that’s most badly in need of catching up to reality. None of this means that formal education, or even graduate degrees aren’t worth obtaining, of course. But no one should take the need for a Master’s or a PhD for granted: you might want to rethink your strategy.
The potentials that exist in the application of blockchain technology and the Internet of Things are huge. The implementation of a blockchain decentralized approach to an entire IoT network can help in many ways. It will ensure proper security by ensuring the privacy and protection of data at all levels. In addition, blockchain can help resolve scalability issues and provide an effective functioning of the system as well. This article comes with an emphasis on network security.
These are 5 Kinds of problems where Deep Learning is applicable, namely Generative Computational Modeling, Complex Predictive Systems, Adaptive Imitation Generative Design Exploration, and Decision Support Systems. These five areas are all advanced new capabilities that could not have happened without the arrival of Deep Learning. It would be unrealistic for a company to specialize in all five areas. Any company focusing on a DL product must specialize in any one of these five areas. Furthermore, each of these five areas supports different verticals.
Open source computing is hugely important to software development. It is the model that everyone benefits from. The open source foundations that support this development play a crucial role. Open source foundations have emerged to help sustain and manage open source projects. These foundations provide space for companies and people with a stake in open source software (OSS) project to come together. Their status as independent, non-profit entities provides neutral ground for competing companies to work together. Let’s see who’s behind many of the tools software developers and data scientists use every day.
What steps are you taking to navigate your organization through one of the biggest business shifts ever? What are the company’s Digital Transformation goals and how will the transformation is successful? Are you considering the “humanscape” of the organization as changes are being integrated into the business? Digital transformation is enabling organizations to open up new sales channels, develop new markets and grow opportunities. Those that have made this transformation have proven increased revenues, and improved efficiencies. But to fully embrace digital transformation, enterprises must start with capturing, integrating, and utilize quality data.
A data set is called imbalanced if it contains many more samples from one class than from the rest of the classes. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class) while other classes make up the majority. There are many reasons why a dataset might be imbalanced: the category one is targeting might be very rare in the population, or the data might simply be difficult to collect.
Bringing new technologies and devices aboard is non-negotiable for businesses these days. Whether it’s cloud computing for data access or a new productivity app that keeps every member of a team safe, technology is helping us do more with less — and remain profitable as competition heats up. But the data powering today’s business technology introduces potential risk too. Here’s a look at how to remain security-minded as you figure out how to make your business data more mobile and accessible.
While Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) are becoming more important for businesses due to their applications in Computer Vision (CV) and Natural Language Processing (NLP), Reinforcement Learning (RL) as a framework for computational neuroscience to model decision making process seems to be undervalued. Besides, there seems to be very little resources detailing how RL is applied in different industries. Despite the criticisms about RL’s weaknesses, RL should never be neglected in the space of corporate research given its huge potentials in assisting decision making.