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All about Gradient Descent that One Needs to Know in the Context of Machine Learning

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In our everyday life, we are optimizing variables all the time based on our personal choices, requirements, and preferences even unconsciously. We use optimization techniques all day, for instance, we choose the shortest route to avoid traffic when going to work, figure out and decide to take a quick walk around the campus during our free time, and schedule cabs in advance to reach on time. Most of the optimizations we do are about saving time because time is very important in today’s world. 

Optimization has become an integrated part of our lives and as our lives revolve around technologies, it has also become an integrated part of technological products. Optimization is the ultimate goal present at the heart of all statistical and Machine Learning techniques that are widely used in data science. As a result optimization algorithms have been created; one such algorithm is Gradient Descent in Machine Learning

What is Gradient Descent?

Gradient Descent is an optimization algorithm that is used for finding the minimum of a function. In terms of Machine Learning, it is used for updating parameters in a model. Based on the algorithms that were used, parameters can be of various types – coefficients (in linear regression) and weights (in neural networks). To understand the concept of Gradient Descent through a real-life example, think of a hypothetical situation in which a person has to descend into a valley blindfolded. Anyone in a situation like this would firstly take careful steps to find a slope. Once they find a downward slope, they’ll follow it and repeat their steps until they’ve completely descended into the valley, in other words, reached the minima.

This is exactly what the Gradient Descent Algorithm does in a machine. The inclination or slope is the cost function and the role of Gradient Descent is to provide direction and velocity (rate of learning) of movement to find a point where the cost is minimum or in other words, the minima of the cost function. A cost function is“a function that maps an event or values of one or more variables onto a real number intuitively representing some ‘cost’ associated with the event.”

The Application of Gradient Descent

The foremost goal of any Machine Learning algorithm is to build a model. A model is a hypothesis, which can be used to find out estimation for Y based on X. For example – Let’s say we have some data about different houses regarding their price, size, material, etc. and we want to predict the cost of a house based on its size. For one house, one may do it by a manual comparative analysis but let’s assume we need to know the cost for many houses. In such cases, we would like to predict a continuous output for a given number of inputs. It’s a regression problem that can be solved by an optimization algorithm of Machine Learning, such as Gradient Descent. Gradient descent can help to find a line of best fit for a given number of inputs (X values) and provide any number of outputs (Y values).

Types of Gradient Descent Algorithms

  • Batch Gradient Descent – It is used to process training examples for all the iterations. It can get computationally expensive when there are a lot of training examples. In such cases, other types of Gradient Descents are preferred.
  • Stochastic Gradient Descent – The word ‘stochastic’ refers to a ‘random probability’. For a random probability, a random selection is done. Thus, in a Stochastic Gradient Descent, instead of selecting the whole data set, random samples are selected for all the iterations. Since this type of Gradient Descent only uses a single sample to perform all the iterations, it is not computationally expensive. The sample is randomly shuffled and selected from the dataset. The parameters get updated after each repetition; therefore, it’s faster than Batch Gradient Descent.
  • Mini Batch Gradient Descent  It is the fastestform ofGradient Descent which can be used even when there is a large number of training examples. It processes the batch at once. It uses very less iterations, regardless of the number of training samples

Challenges Faced in the Execution of Gradient Descent

Gradient Descent is a very useful optimization algorithm but it is not perfect and has some drawbacks. There are many cases in which gradient descent cannot be used or doesn’t perform well. It happens when one faces different sorts of challenges during the execution of Gradient Descent. There are mainly three types of challenges:

Data Challenges

If the data is arranged in a way that causes a non-convex optimization problem then performing Gradient Descent on it becomes troublesome. Gradient Descent only works for data arranged to cause a well-defined convex optimization problem. In a convex optimization problem, there are many minimal points. The lowest point is called the global minimum whereas the rest, are local minima. It is important to reach the global minimum by avoiding local minima. Furthermore, Gradient Descent cannot be used in a saddle point problem as well. In such a problem, the gradient can be zero even for a non-optimal point.

Gradient Challenges

An improper execution while using Gradient Descent can cause several problems such as a vanishing gradient problem. It happens when the size of the gradient is either too large or too small, which leads to no convergence. 

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What Internet Speed Do I Need?

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Paying for an internet service gives you the right to inquire how much speed you will get.

Today, the internet controls our lives, which is why one must subscribe to the best internet service provider, such as Spectrum for a flawless experience. 

It can be quite complicated to know how much internet speed you need to carry out every day online tasks. You may find yourself asking the same question repeatedly whenever you are planning to get a new internet service package or paying the bill.

It is not simple to select the broadband service plan which will be the best for you and your household. Multiple factors come into consideration, such as house size, people, area, online tasks, and so on. 

In the past 2-3 years a massive increase in the demand for the best home internet service providers is seen. The primary reason behind it is the increase in work from homes jobs, and online schooling, which was started shortly after covid-19. 

Keeping in mind all the crucial factors, you should go with the best internet service provider in the US. It does not matter in which state you reside; it is compulsory to understand your online needs as it helps you decide what internet package will work well for you.

When Should You Update Your Internet Plan?

Has your screen ever frozen while you reached the favorite part of your movie on Netflix? Or the web pages started taking forever to load when your mom started a skype call with her foreign friend? 

These are the early signs to inform you that it’s time to update your internet plan as it is not able to entertain all the users in the house simultaneously. To help you see if you really need faster internet service, we asked the broadband experts working at the top internet service provider companies to get a better idea. 

Bellow, you can have a look at what they stated to better understand when it’s the time for you to upgrade your internet plan.

What Internet Speeds Do You Need?

For Netflix

You require an internet speed of 15 Mbps or more to stream content in HD or 4K. In addition, you will need more speed if you stream Netflix on multiple devices at the same time. The same will be the case for all other streaming services, such as Hulu, Disney+, HBO Max, and Amazon Prime Video.

For Multiple Internet Compatible Devices

It is only obvious that you will be needing a much stronger internet plan if you wish to use multiple internet-compatible devices simultaneously. You may be needing such devices for various reasons like online streaming, gaming, live television, and so on. The ideal internet plan for the said case would have to be somewhere around 200 Mbps+. The perfect speed for data-hungry households in the US.

Consider Gigabit Internet Speeds

Those who are in the IT industry understand the need for a fast internet connection no matter what time of the day it is. This is why they should consider gigabit-speed internet plans for the seamless availability of ultra-fast internet speeds.

Know Your Internet Speeds

Using various online internet speed test websites, you can double-check if you are getting what you are paying for. If not, you can contact your internet service provider and discuss the issue with him.

Ensure to Use Updated Equipment

Not every time it is the ISP that causes the drop in internet speeds. You must make sure that you are using the latest and updated Wi-Fi routers, modems, etc. If they are not the latest or up-to-date, they may cause your internet speeds to slow down.

Conclusion

There you go, folks! I hope this post helped you realize how much internet speed you need to work smoothly on the web. We understand that deciding the right internet service provider is a process that is not as simple as it seems, but we tried to make it easy for you.

It is a good idea to have the number of devices in mind while subscribing to the best internet service provider. Moreover, we recommend going for the one that provides other services with the internet.

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A Guide to Better Supporting Tech Use in Your Business

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If you own a business, you might find that you are not supporting its tech use enough and that this is having a detrimental impact on your company as a whole, from its productivity to its ability to adapt to the future of its industry. If this is the case, here is a guide that will help you to better support tech use within your company in 2023.

Get a Stronger Internet Connection

One of the best steps that you can take to better support tech use in your business is to acquire a stronger internet connection for it. A stronger internet connection will ensure that the software that you purchase is able to run smoothly and that a great number of employees can use your internet and your computers without any issues, such as a lag in internet speed. If you are struggling to find a strong internet connection in your local area, you should consider looking at the options that are provided by companies like Gateway Fiber. They will be able to provide your company with a fiber internet and Wi-Fi mesh network that can help to propel your business into the future.

Get Plug Sockets and Charging Points

You should also ensure that there are enough plug sockets and charging points around your business before introducing a lot of new technology. Installing a large number of sockets around your office will mean that a lot of gadgets can be used at once and connected to the electricity, such as laptops and computers. Not only this, but charging points in accessible places will mean that your employees can use their tech non-stop without running into problems because the gadget they are using is not charged up enough. This can help to make technology a more integral part of your business.

Find a Great Server

It would help if you also looked around for great servers for your company. There are many different types of servers that you may need within your business. For instance, file servers can help you to store a range of documents within a digital space, and a web server will ensure that you will be able to gain access to webpages whenever you need to, and the best web servers will ensure that these webpages load extremely quickly so that you are able to complete your work tasks in no time at all.

Look for IT Companies

It would be best if you also looked around for external IT companies who can help you get the IT infrastructure you need and support your tech use as it grows. Finding external support is vital for those who do not have onsite IT teams or for business people who are not very tech-savvy themselves, as IT companies can ensure that problems are sorted out quickly, that your tech remains secure, and that there is a source of professional knowledge and information on hand at all times to answer queries and concerns. This is extremely beneficial.

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Data science: The essential technical skills

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Data science is one of the most important roles of our time. Organizing vast amounts of cluttered, nonsensical data into clean, actionable information is an incredibly valuable skill, which puts you in an enviable position for work opportunities. Data science helps businesses from various industries make data-driven decisions, increase profits and streamline their processes. So, if you’re thinking a career in data science may be for you, let’s take a closer look at what data science is and the critical skills you need to succeed in the field.

What is data science?

Data science is an academic field that combines statistics, computing, scientific processes, algorithms, mathematics, artificial intelligence and computer engineering to analyze vast amounts of data to extract valuable information. The information extrapolated in data science is turned into easy to understand material so businesses can use it to make data-driven decisions. Now that we know what data science is, what are some typical roles you might have as a data science specialist?

What does a career in data science look like?

There are many exciting career paths you can take as a data scientist, including:

  • Data analyst
  • Data manager
  • Data architect
  • Business analyst
  • Data scientist
  • Machine learning engineer
  • Statistician

These are only a handful of the possible data science roles. There are numerous critical data science skills needed for a successful career in this field. One of the easiest ways to obtain these skills is through a program such as Baylor University’s Online Masters in Computer Science degree track in Data Science. This program helps students to develop the specialized skills needed to excel in the data science field.

Essential data science skills

The exact skills you need for a data science role will depend on what you do and where you work. However, there are some skills common across all data science roles, such as:

Programming

Programming is an essential data science skill and is a necessary aspect of any data science role. Programming knowledge is required to analyze and manage large amounts of data. Data scientists often have to create machine-learning algorithms to predict outcomes using data; so, a deep understanding of programming is required to complete them.

Programming languages to learn include Python, SAS, R and SQL. Python is one of the most popular programming tools in data science due to its vast libraries, flexibility and ease of use. Whereas SQL is used specifically for databases when working with big data.

Big data

All data scientists need a deep understanding of big data. The amount of data we have access to is only increasing, and working with tools such as Spark and NoSQL is vital when processing large data sets that cannot be handled with standard data handling tools.

Machine learning

Understanding machine learning is one of the most critical skills you need as a data scientist, and you’ll need to learn both machine learning and deep learning thoroughly to succeed in the field. Machine learning knowledge helps you gather data more efficiently and predict future data set outcomes. Types of machine learning concepts to master include: linear regressions, logistics regression, decision trees and random forest algorithms. These concepts help create real-time data processing algorithms, which increase the accuracy of future outcome predictions. 

Statistical analysis

Statistical analysis helps you create machine learning algorithms. Statistics and probability are required, and concepts such as linear regression help you collect, interpret and organize data. Factors such as mean, median, mode, variance and standard deviation are all components of statistical analysis that you should learn for data science. At a minimum, descriptive statistics and probability theory are essential for data scientists.

Database management

Database management involves cleaning and organizing large amounts of complex data into organized, readable information that is categorized into patterns and trends. Doing this is often time-consuming but remains vital to making data-driven decisions. Database management, or data wrangling, as it is sometimes called, allows you to present data that can be interpreted.

Interpersonal skills

In addition to the technical skills and specialized knowledge required to be a data scientist, there are some crucial soft skills, such as:

  • Critical thinking
  • Public speaking
  • Empathy
  • Attention to detail
  • Effective communication
  • Active listening
  • Feedback sharing

Public speaking allows you to present information to stakeholders, convey your ideas clearly and show confidence in various professional environments. Empathy can help you understand how your information can impact stakeholders and the business. Effective communication skills help you work with colleagues, land jobs, make presentations to owners and share your ideas. All these skills won’t only help you in data science but in all aspects of your life.

Cloud computing

Cloud computing is prominent in many industries and an important area of data science. Cloud computing can be used to analyze data stored in cloud-based platforms and provides professionals with cloud-based data frameworks that are key to advancing technology.

Data visualization

Data visualization is a key component of data science that is used to extract and represent data in a clear, understandable form. Taking that non-sensical data and turning it into charts, graphs, infographics, plots and animations makes it easy to understand for stakeholders and is one of the main ways in which you will communicate your findings to decision-makers.

Domain knowledge

Domain knowledge is also critical to relate your data to your respective field. For instance, you may specialize in fintech or engineering. Being knowledgeable in your specific domain will help you translate the data you analyze to respective stakeholders more clearly.

Final words

Data science is a highly sought after career, and learning the essential data science skills puts you in a formidable position for employability as you have the skills to help businesses to make decisions and succeed. You’ll use a combination of soft interpersonal skills and specialized data analytical skills, programming, domain knowledge, database management and machine learning. Together, these skills will prepare you to launch a career in data science.

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